Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

Published: 23 October 2024 Publication History

Abstract

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.

References

[1]
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi. 2021. A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion 76 (2021), 243–297.
[2]
Hajar Abedi, Ahmad Ansariyan, Plinio P. Morita, Alexander Wong, Jennifer Boger, and George Shaker. 2023. AI-powered non-contact in-home gait monitoring and activity recognition system based on mm-wave FMCW radar and cloud computing. IEEE Internet of Things Journal 10, 11 (2023), 9465–9481.
[3]
Fadel Adib, Hongzi Mao, Zachary Kabelac, Dina Katabi, and Robert C Miller. 2015. Smart homes that monitor breathing and heart rate. In Proceedings of the Annual ACM Conference on Human Factors in Computing Systems. ACM, New York, NY, 837–846.
[4]
Hamsa M. Ahmed and Ahmed Noori Rashid. 2022. Wireless sensor network technology and adoption in healthcare: A review. Proceedings of the American Institute of Physics 2400 (2022), 020021.
[5]
Ali S. Al-Mejrad. 2021. Design and implementation of in-home real-time wireless fever monitoring in pediatrics during the COVID-19 pandemic. Journal of Robotics Networks Artificial Life 8, 1 (2021), 37–40.
[6]
Mohammed Gh Al Zamil, Majdi Rawashdeh, Samer Samarah, M Shamim Hossain, Awny Alnusair, and Sk Md Mizanur Rahman. 2017. An annotation technique for in-home smart monitoring environments. IEEE Access 6 (2017), 1471–1479.
[7]
Ridwan Alam, Joshua Dugan, Nutta Homdee, Neeraj Gandhi, Benjamin Ghaemmaghami, Harshitha Meda, Azziza Bankole, Martha Anderson, Jiaqi Gong, Tonya Smith-Jackson, Azziza Bankole, Martha S. Anderson, and John Lach. 2017. BESI: Reliable and heterogeneous sensing and intervention for in-home health applications. In Proceedings of the IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, Vol. 2, 147–156.
[8]
Ahlam Alami, Laila Benhlima, and Slimane Bah. 2018. A study of security requirements in wireless sensor networks for smart home healthcare systems. In Proceedings of the International Conference on Smart City Applications. ACM, New York, NY, 1–8.
[9]
Malik Bader Alazzam, Fawaz Alassery, and Ahmed Almulihi. 2021. A novel smart healthcare monitoring system using machine learning and the Internet of Things. Wireless Communications and Mobile Computing 2021 (2021), 1–7.
[10]
Matthew E. Allen, Taya Irizarry, Julian Einhorn, Thomas W. Kamarck, Brian P. Suffoletto, Lora E. Burke, Bruce L. Rollman, and Matthew F. Muldoon. 2019. SMS-facilitated home blood pressure monitoring: A qualitative analysis of resultant health behavior change. Patient Education and Counseling 102, 12 (2019), 2246–2253.
[11]
Mazin Alshamrani. 2022. IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey. Journal of King Saud University-Computer and Information Sciences 34, 8 (2022), 4687–4701.
[12]
Moeness G. Amin, Yimin D. Zhang, Fauzia Ahmad, and K. C. Dominic Ho. 2016. Radar signal processing for elderly fall detection: The future for in-home monitoring. IEEE Signal Processing Magazine 33, 2 (2016), 71–80.
[13]
Alireza Amirshahi and Matin Hashemi. 2019. ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices. IEEE Transactions on Biomedical Circuits and Systems 13, 6 (2019), 1483–1493.
[14]
Ayesha Amjad, Piotr Kordel, and Gabriela Fernandes. 2023. A review on innovation in healthcare sector (telehealth) through artificial intelligence. Sustainability 15, 8 (2023), 6655.
[15]
Prithila Angkan, Behnam Behinaein, Zunayed Mahmud, Anubhav Bhatti, Dirk Rodenburg, Paul Hungler, and Ali Etemad. 2024. Multimodal brain-computer interface for in-vehicle driver cognitive load measurement: Dataset and baselines. IEEE Transactions on Intelligent Transportation Systems 25, 6 (2024), 5949–5964.
[16]
Arman Anzanpour, Amir-Mohammad Rahmani, Pasi Liljeberg, and Hannu Tenhunen. 2015. Internet of things enabled in-home health monitoring system using early warning score. In Proceedings of the International Conference on Eireless Mobile Communication and Healthcare. ACM, New York, NY, 174–177.
[17]
Mohammad Arar, Chuloh Jung, Jihad Awad, and Afaq Hyder Chohan. 2021. Analysis of smart home technology acceptance and preference for elderly in Dubai, UAE. Designs 5, 4 (2021), 70.
[18]
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, Raja Chatila and Francisco Herrera. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58 (2020), 82–115.
[19]
Gagangeet Singh Aujla and Anish Jindal. 2020. A decoupled blockchain approach for edge-envisioned IoT-based healthcare monitoring. IEEE Journal on Selected Areas in Communications 39, 2 (2020), 491–499.
[20]
Ibukun Awolusi, Eric Marks, and Matthew Hallowell. 2018. Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Automation in Construction 85 (2018), 96–106.
[21]
Lu Bai, Fabio Ciravegna, Raymond Bond, and Maurice Mulvenna. 2020. A low cost indoor positioning system using bluetooth low energy. IEEE Access 8 (2020), 136858–136871.
[22]
Uabua Bakar, Hemant Ghayvat, S. F. Hasanm, and Subhas Chandra Mukhopadhyay. 2016. Activity and Anomaly Detection in Smart Home: A Survey. Springer International Publishing, 191–220.
[23]
Ryoji Ban, Katsuhiko Kaji, Kei Hiroi, and Nobuo Kawaguchi. 2015. Indoor positioning method integrating pedestrian dead reckoning with magnetic field and WiFi fingerprints. In Proceedings of the International Conference on Mobile Computing and Ubiquitous Networking. IEEE, 167–172.
[24]
Juan Camilo Lopera Bedoya and Jose Lisandro Aguilar Castro. 2024. Explainability analysis in predictive models based on machine learning techniques on the risk of hospital readmissions. Health and Technology 14, 1 (2024), 93–108.
[25]
Behnam Behinaein, Anubhav Bhatti, Dirk Rodenburg, Paul Hungler, and Ali Etemad. 2021. A transformer architecture for stress detection from ECG. In International Symposium on Wearable Computers. ACM, New York, NY, 132–134.
[26]
J. Michael Bertsch and Stephen P. Gent. 2020. Design of a wearable health monitoring system for in-home and emergency use. In Proceedings of the Frontiers in Biomedical Devices, Vol. 83549, V001T09A008.
[27]
Anubhav Bhatti, Behnam Behinaein, Paul Hungler, and Ali Etemad. 2022. AttX: Attentive cross-connections for fusion of wearable signals in emotion recognition. arXiv:2206.04625. Retrieved from
[28]
Tanima Bhowmik, Rohan Mojumder, Dibyendu Ghosh, and Indrajit Banerjee. 2022. IoT based smart home-health monitoring system using dempster-shafer evidence theory for pandemic situation. In Proceedings of the International Conference on Distributed Computing and Networking. ACM, New York, NY, 260–265.
[29]
Mingyun Bian, Guanghui He, Guorui Feng, Xinpeng Zhang, and Yanli Ren. 2024. Verifiable privacy-preserving heart rate estimation based on LSTM. IEEE Internet of Things Journal 11, 1 (2024), 1719–1731.
[30]
Simone Bianco, Luigi Celona, Gianluigi Ciocca, Davide Marelli, Paolo Napoletano, Stefano Yu, and Raimondo Schettini. 2021. A smart mirror for emotion monitoring in home environments. Sensors 21, 22 (2021), 7453.
[31]
Christopher M. Bishop. 2006. Machine Learning. Retrieved from link.springer.com/book/9780387310732
[32]
Igor Bisio, Alessandro Delfino, Fabio Lavagetto, and Andrea Sciarrone. 2016. Enabling IoT for in-home rehabilitation: Accelerometer signals classification methods for activity and movement recognition. IEEE Internet of Things Journal 4, 1 (2016), 135–146.
[33]
Dwaipayan Biswas, Neide Simões-Capela, Chris Van Hoof, and Nick Van Helleputte. 2019. Heart rate estimation from wrist-worn photoplethysmography: A review. IEEE Sensors Journal 19, 16 (2019), 6560–6570.
[34]
Costas Boletsis, Simon McCallum, and Brynjar Fowels Landmark. 2015. The use of smartwatches for health monitoring in home-based dementia care. In Proceedings of the Human Aspects of IT for the Aged Population. Design for Everyday Life: First International Conference. 15–26.
[35]
Christopher P. Bonafide, A. Russell Localio, Daria F. Ferro, Evan W. Orenstein, David T. Jamison, Chris Lavanchy, and Elizabeth E. Foglia. 2018. Accuracy of pulse oximetry-based home baby monitors. JAMA 320, 7 (2018), 717–719.
[36]
Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G. Willcocks. 2022. Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 11 (2022), 7327–7347.
[37]
Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the internet of things. In Proceedings of the Workshop on Mobile Cloud Computing. ACM, New York, NY, 13–16.
[38]
Amine Boulemtafes and Nadjib Badache. 2016. Design of wearable health monitoring systems: An overview of techniques and technologies. In mHealth Ecosystems and Social Networks in Healthcare. Ramesh Sharda (Ed.), Springer, 79–94.
[39]
Leo Breiman. 2001. Random forests. Machine Learning 45 (2001), 5–32.
[40]
Renato Bulcão-Neto, Paulo Teixeira, Bruno Lebtag, Valdemar Graciano-Neto, Alessandra Macedo, and Bernard Zeigler. 2023. Simulation of IoT-oriented fall detection systems architectures for in-home patients. IEEE Latin America Transactions 21, 1 (2023), 16–26.
[41]
J. V. Candy. 2021. Accelerometer Modeling in the State-Space. Technical Report. Lawrence Livermore National Lab.
[42]
Jose A. Cantoral-Ceballos, Nurgiyatna Nurgiyatna, Paul Wright, John Vaughan, Christine Brown-Wilson, Patricia J. Scully, and Krikor B. Ozanyan. 2015. Intelligent carpet system, based on photonic guided-path tomography, for gait and balance monitoring in home environments. IEEE Sensors Journal 15, 1 (2015), 279–289.
[43]
José-Borja Castillo-Sánchez, Eduardo Casilari, Jose Manuel Cano-García, Eva González-Parada, and Francisco J. González-Cañete. 2022. A low-cost home monitoring architecture for human activity recognition using smartwatches. In Proceedings of the International Conference on Communications and Networking. IEEE, 172–177.
[44]
Saiteja Prasad Chatrati, Gahangir Hossain, Ayush Goyal, Anupama Bhan, Sayantan Bhattacharya, Devottam Gaurav, and Sanju Mishra Tiwari. 2022. Smart home health monitoring system for predicting type 2 diabetes and hypertension. Journal of King Saud University-Computer and Information Sciences 34, 3 (2022), 862–870.
[45]
Samir Chatterjee, Jongbok Byun, Kaushik Dutta, Rasmus Ulslev Pedersen, Akshay Pottathil, and Harry Xie. 2018. Designing an Internet-of-Things (IoT) and sensor-based in-home monitoring system for assisting diabetes patients: Iterative learning from two case studies. European Journal of Information Systems 27, 6 (2018), 670–685.
[46]
Kefan Chen, Noah Snavely, and Ameesh Makadia. 2021. Wide-baseline relative camera pose estimation with directional learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3257–3267.
[47]
Mu-Yen Chen. 2021. Establishing a cybersecurity home monitoring system for the elderly. IEEE Transactions on Industrial Informatics 18, 7 (2021), 4838–4845.
[48]
Sinan Chen, Sachio Saiki, and Masahide Nakamura. 2020. Nonintrusive fine-grained home care monitoring: Characterizing quality of in-home postural changes using bone-based human sensing. Sensors 20, 20 (2020), 5894.
[49]
Zhaomin Chen, Chai Kiat Yeo, Bu Sung Lee, and Chiew Tong Lau. 2018. Autoencoder-based network anomaly detection. In Proceedings of the Wireless Telecommunications Symposium. IEEE, 1–5.
[50]
Keith Yu-Kin Cheng, Simon Kwoon-Ho Chow, Vivian Wing-Yin Hung, Carissa Hing-Wai Wong, Ronald Man-Yeung Wong, Charlotte Sau-Lan Tsang, Timothy Kwok, and Wing-Hoi Cheung. 2021. Diagnosis of sarcopenia by evaluating skeletal muscle mass by adjusted bioimpedance analysis validated with dual-energy X-ray absorptiometry. Journal of Cachexia, Sarcopenia and Muscle 12, 6 (2021), 2163–2173.
[51]
Zhengnan Cheng, Xiaohui Li, Hongmei Xu, Di Bao, Chun Mu, and Qiuling Xing. 2023. Incidence of accidental falls and development of a fall risk prediction model among elderly patients with diabetes mellitus: A prospective cohort study. Journal of Clinical Nursing 32, 7–8 (2023), 1398–1409.
[52]
An-Ti Chiang, Qi Chen, Yao Wang, and Mei R. Fu. 2018. Kinect-based in-home exercise system for lymphatic health and lymphedema intervention. IEEE Journal of Translational Engineering in Health and Medicine 6 (2018), 1–13.
[53]
Martina Chirra, Luca Marsili, Linsdey Wattley, Leonard L. Sokol, Elizabeth Keeling, Simona Maule, Gabriele Sobrero, Carlo Alberto Artusi, Alberto Romagnolo, Maurizio Zibetti, and L. Lopiano. 2019. Telemedicine in neurological disorders: Opportunities and challenges. Telemedicine and E-Health 25, 7 (2019), 541–550.
[54]
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24, 240 (2023), 1–113.
[55]
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555. Retrieved from https://doi.org/10.48550/arXiv.1412.3555
[56]
David D. Clark, Kenneth T. Pogran, and David P. Reed. 1978. An introduction to local area networks. Proceedings of the IEEE 66, 11 (1978), 1497–1517.
[57]
Nicholas J. Conn, Karl Q. Schwarz, and David A. Borkholder. 2019. In-home cardiovascular monitoring system for heart failure: comparative study. Journal of Medical Internet Research mHealth and uHealth 7, 1 (2019), e12419.
[58]
A. Feder Cooper, Emanuel Moss, Benjamin Laufer, and Helen Nissenbaum. 2022. Accountability in an algorithmic society: relationality, responsibility, and robustness in machine learning. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency. 864–876.
[59]
An Dang, Toan H. Vu, and Jia-Ching Wang. 2017. A survey of deep learning for polyphonic sound event detection. In Proceedings of the International Conference on Orange Technologies. IEEE, 75–78.
[60]
Ishan Rajendrakumar Dave, Mamshad Nayeem Rizve, Chen Chen, and Mubarak Shah. 2023. TimeBalance: Temporally-invariant and temporally-distinctive video representations for semi-supervised action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2341–2352.
[61]
Vandad Davoodnia and Ali Etemad. 2019. Identity and posture recognition in smart beds with deep multitask learning. In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics. 3054–3059.
[62]
Vandad Davoodnia, Saeed Ghorbani, and Ali Etemad. 2021. In-bed pressure-based pose estimation using image space representation learning. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP ’21). 3965–3969.
[63]
Vandad Davoodnia, Saeed Ghorbani, and Ali Etemad. 2022. Estimating pose from pressure data for smart beds with deep image-based pose estimators. Applied Intelligence 52 (2022), 2119–2133.
[64]
Thiago de Quadros, Andre Eugenio Lazzaretti, and Fábio Kürt Schneider. 2018. A movement decomposition and machine learning-based fall detection system using wrist wearable device. IEEE Sensors Journal 18, 12 (2018), 5082–5089.
[65]
M. Jamal Deen. 2015. Information and communications technologies for elderly ubiquitous healthcare in a smart home. Personal and Ubiquitous Computing 19 (2015), 573–599.
[66]
Emily L. Denton, Soumith Chintala, and Rob Fergus. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 28, 1–9.
[67]
Omer Deperlioglu, Utku Kose, Deepak Gupta, Ashish Khanna, and Arun Kumar Sangaiah. 2020. Diagnosis of heart diseases by a secure internet of health things system based on autoencoder deep neural network. Computer Communications 162 (2020), 31–50.
[68]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https://doi.org/10.48550/arXiv.1810.04805
[69]
Nilanjan Dey, Amira S. Ashour, Fuqian Shi, Simon James Fong, and R. Simon Sherratt. 2017. Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Transactions on Consumer Electronics 63, 4 (2017), 442–449.
[70]
R. C. Dharmik, Shashank Gotarkar, Pooja Dinesh, and Harshal Sant Burde. 2021. An IoT framework for healthcare monitoring system. In Journal of Physics: Conference Series, Vol. 1913, 012145.
[71]
Robert F Dickerson, Enamul Hoque, Ifat Afrin Emi, and John A Stankovic. 2015. Empath2: A flexible web and cloud-based home health care monitoring system. In Proceedings of the ACM International Conference on PErvasive Technologies Related to Assistive Environments, 1–8.
[72]
Virginia Dignum. 2019. Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Vol. 2156. Springer.
[73]
Youcef Djenouri, Ahmed Nabil Belbachir, Alberto Cano, and Asma Belhadi. 2024. Spatio-temporal visual learning for home-based monitoring. Information Fusion 101 (2024), 101984.
[74]
Trong-Hop Do and Myungsik Yoo. 2016. An in-depth survey of visible light communication based positioning systems. Sensors 16, 5 (2016), 678.
[75]
Hiroko H. Dodge, Jian Zhu, Nora C. Mattek, Daniel Austin, Judith Kornfeld, and Jeffrey A. Kaye. 2015. Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PloS One 10, 9 (2015), e0138095.
[76]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929. Retrieved from https://doi.org/10.48550/arXiv.2010.11929
[77]
Alexandru Dregan and David Armstrong. 2011. Cross-country variation in sleep disturbance among working and older age groups: an analysis based on the European Social Survey. International Psychogeriatrics 23, 9 (2011), 1413–1420.
[78]
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel. 2011. Fairness through awareness. In Proceedings of the Innovations in Theoretical Computer Science Conference. ACM, New York, NY, 214–226. DOI:
[79]
Alex Edgcomb and Frank Vahid. 2012. Privacy perception and fall detection accuracy for in-home video assistive monitoring with privacy enhancements. ACM SIGHIT Record 2, 2 (2012), 6–15.
[80]
Chadi El-Hajj and Panayiotis A. Kyriacou. 2020. A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure. Biomedical Signal Processing and Control 58 (2020), 101870.
[81]
Shaker El-Sappagh, Farman Ali, Samir El-Masri, Kyehyun Kim, Amjad Ali, and Kyung-Sup Kwak. 2018. Mobile health technologies for diabetes mellitus: current state and future challenges. IEEE Access 7 (2018), 21917–21947.
[82]
Fatma H. Elgendy, Amany M. Sarhan, and Mahmoud A. M. Alshewimy. 2021. Fog-based remote in-home health monitoring framework. Fog 12, 6 (2021), 247–254.
[83]
Jihed Elouni, Hamdi Ellouzi, Hela Ltifi, and Mounir Ben Ayed. 2020. Intelligent health monitoring system modeling based on machine learning and agent technology. Multiagent and Grid Systems 16, 2 (2020), 207–226.
[84]
Shirin Enshaeifar, Payam Barnaghi, Severin Skillman, David Sharp, Ramin Nilforooshan, and Helen Rostill. 2020. A digital platform for remote healthcare monitoring. In Proceedings of the Companion Proceedings of the Web Conference. ACM, New York, NY, 203–206.
[85]
Shirin Enshaeifar, Ahmed Zoha, Andreas Markides, Severin Skillman, Sahr Thomas Acton, Tarek Elsaleh, Masoud Hassanpour, Alireza Ahrabian, Mark Kenny, Stuart Klein, Helen Rostill, Ramin Nilforooshan, and Payam Barnaghi. 2018. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PloS One 13, 5 (2018), e0195605.
[86]
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 3557–3568.
[87]
Kerry Y. Fang, Jeewani Anupama Ginige, Jim Basilakis, Heidi Bjering, and Bahman Javadi. 2022. Smart homecare research translation into broader practice: Enablers, barriers and directions. IEEE Access 10 (2022), 134726–134743.
[88]
Qiang Fang, Seedahmed S. Mahmoud, Akshay Kumar, Xudong Gu, and Jianming Fu. 2020. A longitudinal investigation of the efficacy of supported in-home post-stroke rehabilitation. IEEE Access 8 (2020), 138690–138700.
[89]
Nada Fares, R. Simon Sherratt, and Imad H. Elhajj. 2021. Directing and orienting ICT healthcare solutions to address the needs of the aging population. Healthcare 9 (2021), 147.
[90]
Zahid Farid, Rosdiadee Nordin, and Mahamod Ismail. 2013. Recent advances in wireless indoor localization techniques and system. Journal of Computer Networks and Communications 2013 (2013), Article 185138.
[91]
Komal Farooq, Hassan Jamil Syed, Samar Othman Alqahtani, Wamda Nagmeldin, Ashraf Osman Ibrahim, and Abdullah Gani. 2022. Blockchain federated learning for in-home health monitoring. Electronics 12, 1 (2022), 136.
[92]
Xiaoce Feng, Ming Dong, Philip Levy, and Yong Xu. 2017. Non-contact home health monitoring based on low-cost high-performance accelerometers. In Proceedings of the IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, 356–364.
[93]
C. Ferraris, R. Nerino, A. Chimienti, G. Pettiti, D. Pianu, G. Albani, C. Azzaro, L. Contin, Veronica Cimolin, and A. Mauro. 2014. Remote monitoring and rehabilitation for patients with neurological diseases. In Proceedings of the International Conference on Body Area Networks, 76–82.
[94]
João J. Ferreira, Cristina I. Fernandes, Hussain G. Rammal, and Pedro M. Veiga. 2021. Wearable technology and consumer interaction: A systematic review and research agenda. Computers in Human Behavior 118 (2021), Article 106710.
[95]
Halit Fidanci, Şencan Buturak, İlker Öztürk, and Zuelfikar Arlier. 2023. Needle electromyography abnormalities in the upper trapezius muscle in neuromuscular disorders. Turkish Journal of Medical Sciences 53, 1 (2023), 233–242.
[96]
Madeleine Flaucher, Michael Nissen, Katharina M. Jaeger, Adriana Titzmann, Constanza Pontones, Hanna Huebner, Peter A. Fasching, Matthias W. Beckmann, Stefan Gradl, and Bjoern M. Eskofier. 2022. Smartphone-based colorimetric analysis of urine test strips for at-home prenatal care. IEEE Journal of Translational Engineering in Health and Medicine 10 (2022), 1–9.
[97]
Shane Forbrigger, Madeleine Liblong, T. C. Davies, Vincent DePaul, Evelyn Morin, and Keyvan Hashtrudi-Zaad. 2023. Considerations for at-home upper-limb rehabilitation technology following stroke: Perspectives of stroke survivors and therapists. Journal of Rehabilitation and Assistive Technologies Engineering 10 (2023), 20556683231171840.
[98]
Petra Friedrich, Maksym Gaiduk, Ángel Serrano Alarcón, Daniel Scherz, Natividad Martinez Madrid, Ralf Seepold, Matthias Gaßner, and Dominik Fuchs. 2022. Assistive health systems for home-dwelling elderly: connecting training and monitoring technologies to a data integration platform. Procedia Computer Science 207 (2022), 3008–3017.
[99]
Wentao Fu, Dongfang Tang, Fuzhi Yang, Jing Wang, Yingting Wu, Xiaoyong Shen, and Wen Gao. 2022. Short-term home remote monitoring of patients after lung cancer surgery. Clinical Surgical Oncology 1, 1 (2022), Article 100004.
[100]
Ye Gao, Meiyi Ma, Kristina Gordon, Karen Rose, Hongning Wang, and John Stankovic. 2020. A monitoring, modeling, and interactive recommendation system for in-home caregivers: Demo abstract. In Proceedings of the Conference on Embedded Networked Sensor Systems. ACM, New York, NY, 587–588.
[101]
Matt W. Gardner and S. R. Dorling. 1998. Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmospheric Environment 32, 14–15 (1998), 2627–2636.
[102]
Mohammad Ghamari, Balazs Janko, R. Simon Sherratt, William Harwin, Robert Piechockic, and Cinna Soltanpur. 2016. A survey on wireless body area networks for ehealthcare systems in residential environments. Sensors 16, 6 (2016), 831.
[103]
Ammar Gharaibeh, Mohammad A Salahuddin, Sayed Jahed Hussini, Abdallah Khreishah, Issa Khalil, Mohsen Guizani, and Ala Al-Fuqaha. 2017. Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys & Tutorials 19, 4 (2017), 2456–2501.
[104]
Juan Pablo Gomez-Arrunategui, Janice J. Eng, and Antony J. Hodgson. 2022. Monitoring arm movements post-stroke for applications in rehabilitation and home settings. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30 (2022), 2312–2321.
[105]
Jiaqi Gong, Karen Moomaw Rose, Ifat Afrin Emi, Janet P Specht, Enamul Hoque, Dawei Fan, Sriram Raju Dandu, Robert F Dickerson, Yelena Perkhounkova, John Lach, and John Anthony Stankovic. 2015. Home wireless sensing system for monitoring nighttime agitation and incontinence in patients with Alzheimer’s disease. In Proceedings of the conference on Wireless Health. ACM, New York, NY, 1–8.
[106]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. Communications of the ACM 63, 11 (2020), 139–144.
[107]
Xiaowei Gu, Plamen Angelov, Ce Zhang, and Peter Atkinson. 2022. A semi-supervised deep rule-based approach for complex satellite sensor image analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5 (2022), 2281–2292.
[108]
Hongyu Guo and Herna L. Viktor. 2004. Learning from imbalanced data sets with boosting and data generation: The databoost-im approach. Sigkdd Explorations Newsletter 6, 1 (2004), 30–39.
[109]
Lingchao Guo, Zhaoming Lu, Shuang Zhou, Xiangming Wen, and Zhihong He. 2020. When healthcare meets off-the-shelf WiFi: A non-wearable and low-costs approach for in-home monitoring. arXiv:2009.09715. Retrieved from https://doi.org/10.48550/arXiv.2009.09715
[110]
Lingchao Guo, Zhaoming Lu, Shuang Zhou, Xiangming Wen, and Zhihong He. 2021. Emergency semantic feature vector extraction from WiFi signals for in-home monitoring of elderly. IEEE Journal of Selected Topics in Signal Processing 15, 6 (2021), 1423–1438.
[111]
Divij Gupta and Ali Etemad. 2023. Privacy-preserving remote heart rate estimation from facial videos. arXiv:2306.01141. Retrived from https://doi.org/10.1109/SMC53992.2023.10394350
[112]
Mohammad Hadian, Xiaohui Liang, Thamer Altuwaiyan, and Mohamed M. E. A. Mahmoud. 2016. Privacy-preserving mhealth data release with pattern consistency. In Proceedings of the Global Communications Conference. IEEE, 1–6.
[113]
Savvas Hadjixenophontos, Anna Maria Mandalari, Yuchen Zhao, and Hamed Haddadi. 2023. PRISM: Privacy preserving healthcare internet of things security management. In Proceedings of the IEEE Symposium on Computers and Communications, 1–5.
[114]
Katie S. Hahm and Brian W. Anthony. 2022. In-home health monitoring using floor-based gait tracking. Internet of Things 19 (2022), Article 100541.
[115]
Gelareh Hajian, Behnam Behinaein, Ali Etemad, and Evelyn Morin. 2022a. Bagged tree ensemble modelling with feature selection for isometric EMG-based force estimation. Biomedical Signal Processing and Control 78 (2022), Article 104012.
[116]
Gelareh Hajian, Ali Etemad, and Evelyn Morin. 2020. Automated channel selection in high-density sEMG for improved force estimation. Sensors 20, 17 (2020), Article 4858.
[117]
Gelareh Hajian, Evelyn Morin, and Ali Etemad. 2022b. Multimodal estimation of endpoint force during quasi-dynamic and dynamic muscle contractions using deep learning. IEEE Transactions on Instrumentation and Measurement 71 (2022), 1–11.
[118]
Pedro C. Hallal, Lars Bo Andersen, Fiona C. Bull, Regina Guthold, William Haskell, and Ulf Ekelund. 2012. Global physical activity levels: surveillance progress, pitfalls, and prospects. The Lancet 380 (2012), 247–257.
[119]
G. M. Harshvardhan, Mahendra Kumar Gourisaria, Manjusha Pandey, and Siddharth Swarup Rautaray. 2020. A comprehensive survey and analysis of generative models in machine learning. Computer Science Review 38 (2020), Article 100285.
[120]
Paige Harvey, Otily Toutsop, Kevin Kornegay, Excel Alale, and Don Reaves. 2020. Security and privacy of medical internet of things devices for smart homes. In Proceedings of the International Conference on Internet of Things: Systems, Management and Security. IEEE, 1–6.
[121]
M. N. Ul Hasan and I. Negulescu. 2020. Wearable technology for baby monitoring: A review. Journal of Textile Engineering & Fashion Technology 6, 112.10 (2020), Article 15406.
[122]
Ibrahim Abaker Targio Hashem, Ibrar Yaqoob, Nor Badrul Anuar, Salimah Mokhtar, Abdullah Gani, and Samee Ullah Khan. 2015. The rise of “big data” on cloud computing: Review and open research issues. Information Systems 47 (2015), 98–115.
[123]
Aboul Ella Hassanien. 2019. Machine Learning Paradigms: Theory and Application. Springer.
[124]
Suining He and S.-H. Gary Chan. 2015. Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys & Tutorials 18, 1 (2015), 466–490.
[125]
Moein Heidari, Amirhossein Kazerouni, Milad Soltany, Reza Azad, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, and Dorit Merhof. 2023. Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 6202–6212.
[126]
Jajack Heikenfeld, Andrew Jajack, Jim Rogers, Philipp Gutruf, Lei Tian, Tingrui Pan, Ruya Li, Michelle Khine, Jintae Kim, and Juanhong Wang. 2018. Wearable sensors: Modalities, challenges, and prospects. Lab on a Chip 18, 2 (2018), 217–248.
[127]
Carolin Hildebrandt, Christian Raschner, and Kurt Ammer. 2010. An overview of recent application of medical infrared thermography in sports medicine in Austria. Sensors 10, 5 (2010), 4700–4715.
[128]
Geoffrey E. Hinton and Ruslan R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504–507.
[129]
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580. Retrieved from https://doi.org/10.48550/arXiv.1207.0580
[130]
Mollie Hobensack, Jiyoun Song, Danielle Scharp, Kathryn H. Bowles, and Maxim Topaz. 2022. Machine learning applied to electronic health record data in home healthcare: A scoping review. International Journal of Medical Informatics 170 (2022), Article 104978.
[131]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.
[132]
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. 2022. Training compute-optimal large language models. arXiv:2203.15556. Retrieved from https://doi.org/10.48550/arXiv.2203.15556
[133]
Satoko Honda, Hyuga Hara, Takayuki Arie, Seiji Akita, and Kuniharu Takei. 2022. A wearable, flexible sensor for real-time, home monitoring of sleep apnea. Iscience 25, 4 (2022), Article 104163.
[134]
M Shamim Hossain and Ghulam Muhammad. 2016. Cloud-assisted industrial internet of things (IIoT)–enabled framework for health monitoring. Computer Networks 101 (2016), 192–202.
[135]
Borui Hou, Jianyong Yang, Pu Wang, and Ruqiang Yan. 2019. LSTM-based auto-encoder model for ECG arrhythmias classification. IEEE Transactions on Instrumentation and Measurement 69, 4 (2019), 1232–1240.
[136]
Chen-Yu Hsu, Aayush Ahuja, Shichao Yue, Rumen Hristov, Zachary Kabelac, and Dina Katabi. 2017. Zero-effort in-home sleep and insomnia monitoring using radio signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1–18.
[137]
Rui Hu, Jie Chen, and Li Zhou. 2022. A transformer-based deep neural network for arrhythmia detection using continuous ECG signals. Computers in Biology and Medicine 144 (2022), Article 105325.
[138]
Shih-Cheng Huang, Anuj Pareek, Malte Jensen, Matthew P. Lungren, Serena Yeung, and Akshay S. Chaudhari. 2023. Self-supervised learning for medical image classification: A systematic review and implementation guidelines. NPJ Digital Medicine 6, 1 (2023), 74.
[139]
David H. Hubel and Torsten N. Wiesel. 1968. Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology 195, 1 (1968), 215–243.
[140]
Syed Mahmudul Huq, Rytis Maskeliūnas, and Robertas Damaševičius. 2024. Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: A systematic review. Disability and Rehabilitation: Assistive Technology 19, 3 (2024), 1059–1078.
[141]
Tanveer Hussain, Khan Muhammad, Salman Khan, Amin Ullah, Mi Young Lee, and Sung Wook Baik. 2019. Intelligent baby behavior monitoring using embedded vision in IoT for smart healthcare centers. Journal of Artificial Intelligence and Systems 1, 1 (2019), 110–124.
[142]
Augustine Ikpehai, Bamidele Adebisi, Khaled M. Rabie, Kelvin Anoh, Ruth E. Ande, Mohammad Hammoudeh, Haris Gacanin, and Uche M. Mbanaso. 2018. Low-power wide area network technologies for Internet-of-Things: A comparative review. IEEE Internet of Things Journal 6, 2 (2018), 2225–2240.
[143]
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and koray kavukcuoglu. 2015. Spatial transformer networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 28. Curran Associates, Inc, 1–9.
[144]
Amin Jalali and Minho Lee. 2019. Atrial fibrillation prediction with residual network using sensitivity and orthogonality constraints. IEEE Journal of Biomedical and Health Informatics 24, 2 (2019), 407–413.
[145]
Amin Jalali and Minho Lee. 2023. Adversarial Lagrangian integrated contrastive embedding for limited size datasets. Neural Networks 160 (2023), 122–131.
[146]
Abdul Q. Javaid, Rishabh Gupta, Alex Mihalidis, and S. Ali Etemad. 2017. Balance-based time-frequency features for discrimination of young and elderly subjects using unsupervised methods. In Proceedings of the IEEE International Conference on Biomedical & Health Informatics, 453–456.
[147]
M. Jayalakshmi and V. Gomathi. 2020. Pervasive health monitoring through video-based activity information integrated with sensor-cloud oriented context-aware decision support system. Multimedia Tools and Applications 79 (2020), 3699–3712.
[148]
Long Jiang, Bin Gao, Jun Gu, Yuanpeng Chen, Zhao Gao, Xiaole Ma, Keith M. Kendrick, and Wai Lok Woo. 2018. Wearable long-term social sensing for mental wellbeing. IEEE Sensors Journal 19 (2018), 8532–8542.
[149]
Longlong Jing and Yingli Tian. 2020. Self-supervised visual feature learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 11 (2020), 4037–4058.
[150]
Yanguo Jing, Mark Eastwood, Bo Tan, Alexandros Konios, Abdul Hamid, and Mark Collinson. 2017. An intelligent well-being monitoring system for residents in extra care homes. In Proceedings of the International Conference on Internet of Things and Machine Learning. ACM, New York, NY, 1–6.
[151]
Hlengekile Jita and Vreda Pieterse. 2018. A framework to apply the internet of things for medical care in a home environment. In Proceedings of the International Conference on Cloud Computing and Internet of Things. ACM, New York, NY, 45–54.
[152]
J. T. Thirukrishna, M. V. Aishwarya, Mansi Singh, B. Mounisha, and Naksha Kaveri. 2021. A survey on instantaneous data transmission in wireless sensor networks for healthcare monitoring. Research Square (2021), 1–19.
[153]
Zhizhong Kang, Juntao Yang, Zhou Yang, and Sai Cheng. 2020. A review of techniques for 3d reconstruction of indoor environments. International Journal of Geo-Information 9, 5 (2020), 330.
[154]
Alexander Kazantsev, Julia Ponomareva, Pavel Kazantsev, Rafael Digilov, and Ping Huang. 2012. Development of e-health network for in-home pregnancy surveillance based on artificial intelligence. In Proceedings of IEEE International Conference on Biomedical and Health Informatics, 82–84.
[155]
Pantea Keikhosrokiani and Nor Saralyna Azwa Binti Kamaruddin. 2022. IoT-based in-hospital-in-home heart disease remote monitoring system with machine learning features for decision making. In Connected e-Health: Integrated IoT and Cloud Computing. Sushruta Mishra, Alfonso González-Briones, Akash Kumar Bhoi, Pradeep Kumar Mallick, and Juan M. Corchado (Eds.), Cham, 349–369.
[156]
Rabia Khan, Pardeep Kumar, Dushantha Nalin K. Jayakody, and Madhusanka Liyanage. 2019. A survey on security and privacy of 5G technologies: Potential solutions, recent advancements, and future directions. IEEE Communications Surveys & Tutorials 22, 1 (2019), 196–248.
[157]
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. 2022. Transformers in vision: A survey. ACM Computing Surveys 54, 10s (2022), 1–41.
[158]
Daejin Kim, Hongyi Bian, Carl K. Chang, Liang Dong, and Jennifer Margrett. 2022. In-home monitoring technology for aging in place: Scoping review. Interactive Journal of Medical Research 11, 2 (2022), e39005.
[159]
Diederik P. Kingma and Max Welling. 2022. Auto-encoding variational bayes. arXiv:1312.6114. Retrieved from https://doi.org/10.48550/arXiv.1312.6114
[160]
Sotiris B. Kotsiantis. 2013. Decision trees: A recent overview. Artificial Intelligence Review 39 (2013), 261–283.
[161]
L. Koval, J. Vaňuš, and P. Bilík. 2016. Distance measuring by ultrasonic sensor. IFAC-PapersOnLine 49, 25 (2016), 153–158.
[162]
Rayan Krishnan, Pranav Rajpurkar, and Eric J. Topol. 2022. Self-supervised learning in medicine and healthcare. Nature Biomedical Engineering 6, 12 (2022), 1346–1352.
[163]
Stein Kristiansen, Konstantinos Nikolaidis, Thomas Plagemann, Vera Goebel, Gunn Marit Traaen, Britt Øverland, Lars Aakerøy, Tove-Elizabeth Hunt, Jan Pål Loennechen, Sigurd Loe Steinshamn, Christina Holt Bendz, Ole-Gunnar Anfinsen, Lars Gullestad, and Harriet Akre. 2021. Machine learning for sleep apnea detection with unattended sleep monitoring at home. ACM Transactions on Computing for Healthcare 2, 2 (2021), 1–25.
[164]
Nicolas Kube. 2018. Blockchain basics: a non-technical introduction in 25 steps. Financial Markets and Portfolio Management 8 (2018), 329–331.
[165]
K. S. Santhosh Kumar, J. Hanumantappa, and Balachandra G. Chikkoppa. 2021. Advanced social internet of things for real time monitoring of diabetics patient in healthcare system. In Proceedings of the International Conference on Smart Generation Computing, Communication and Networking. IEEE, 1–8.
[166]
Ravi Kumar. 2023. Establishment of telemedicine architecture in IoT based smart home security system for health monitoring system. In Proceedings of the International Conference on Innovation in Technology. IEEE, 1–8.
[167]
Jayakanth Kunhoth, AbdelGhani Karkar, Somaya Al-Maadeed, and Abdulla Al-Ali. 2020. Indoor positioning and wayfinding systems: A survey. Human-centric Computing and Information Sciences 10, 1 (2020), 1–41.
[168]
Shinjae Kwon, Hojoong Kim, and Woon-Hong Yeo. 2021. Recent advances in wearable sensors and portable electronics for sleep monitoring. Iscience 24, 5 (2021), 102461.
[169]
Oscar D. Lara and Miguel A. Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys & Tutorials 15, 3 (2012), 1192–1209.
[170]
David L. Larkai and Ruiheng Wu. 2015. Wireless heart rate monitor in personal emergency response system. In Proceedings of the IEEE International Symposium on Design and Diagnostics of Electronic Circuits & Systems, 299–300.
[171]
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436.
[172]
Beom-Chan Lee, Junmo An, Jiyeon Kim, and Eugene C. Lai. 2022. Performing dynamic weight-shifting balance exercises with a smartphone-based wearable telerehabilitation system for home Use by individuals with parkinson’s aisease: A proof-of-concept study. IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 (2022), 456–463.
[173]
Deok-Won Lee, Kooksung Jun, Khawar Naheem, and Mun Sang Kim. 2021. Deep neural network–based double-check method for fall detection using IMU-l sensor and RGB camera data. IEEE Access 9 (2021), 48064–48079.
[174]
Ho Seung Lee, Byeongju Noh, Seong Uk Kong, Yong Ha Hwang, Ha-Eun Cho, Yongmin Jeon, and Kyung Cheol Choi. 2023. Fiber-based quantum-dot pulse oximetry for wearable health monitoring with high wavelength selectivity and photoplethysmogram sensitivity. NPJ Flexible Electronics 7, 1 (2023), 15.
[175]
Sunghoon I. Lee, Catherine P. Adans-Dester, Matteo Grimaldi, Ariel V. Dowling, Peter C. Horak, Randie M. Black-Schaffer, Paolo Bonato, and Joseph T. Gwin. 2018. Enabling stroke rehabilitation in home and community settings: a wearable sensor-based approach for upper-limb motor training. IEEE Journal of Translational Engineering in Health and Medicine 6 (2018), 1–11.
[176]
Bryan Lim and Stefan Zohren. 2021. Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A 379 (2021), Article 20200209.
[177]
Feng Lin, Yan Zhuang, Chen Song, Aosen Wang, Yiran Li, Changzhan Gu, Changzhi Li, and Wenyao Xu. 2016. SleepSense: A noncontact and cost-effective sleep monitoring system. IEEE Transactions on Biomedical Circuits and Systems 11, 1 (2016), 189–202.
[178]
Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu. 2022. A survey of transformers. AI Open 3 (2022), 111–132.
[179]
Jixin Liu, Rong Tan, Guang Han, Ning Sun, and Sam Kwong. 2020. Privacy-preserving in-home fall detection using visual shielding sensing and private information-embedding. IEEE Transactions on Multimedia 23 (2020), 3684–3699.
[180]
Kai-Chun Liu, Kuo-Hsuan Hung, Chia-Yeh Hsieh, Hsiang-Yun Huang, Chia-Tai Chan, and Yu Tsao. 2021a. Deep-learning-based signal enhancement of low-resolution accelerometer for fall detection systems. IEEE Transactions on Cognitive and Developmental Systems 14, 3 (2021), 1270–1281.
[181]
Shuangjun Liu and Sarah Ostadabbas. 2023. Pressure eye: In-bed contact pressure estimation via contact-less imaging. Medical Image Analysis 87 (2023), Article 102835.
[182]
Yun Liu and Siqing Du. 2018. Psychological stress level detection based on electrodermal activity. Behavioural Brain Research 341 (2018), 50–53.
[183]
Yiming Liu, Binjie Qin, Rong Li, Xintong Li, Anqi Huang, Haifeng Liu, Yisong Lv, and Min Liu. 2021b. Motion-robust multimodal heart rate estimation using BCG fused remote-PPG with deep facial ROI tracker and pose constrained Kalman filter. IEEE Transactions on Instrumentation and Measurement 70 (2021), 1–15.
[184]
Tatjana Loncar-Turukalo, Eftim Zdravevski, José Machado da Silva, Ioanna Chouvarda, and Vladimir Trajkovik. 2019. Literature on wearable technology for connected health: Scoping review of research trends, advances, and barriers. Journal of Medical Internet Research 21, 9 (2019), e14017.
[185]
Yuri Álvarez López, María Elena de Cos Gómez, and Fernando Las-Heras Andrés. 2017. A received signal strength RFID-based indoor location system. Sensors and Actuators A: Physical 255 (2017), 118–133.
[186]
Zelun Luo, Jun-Ting Hsieh, Niranjan Balachandar, Serena Yeung, Guido Pusiol, Jay Luxenberg, Grace Li, Li-Jia Li, N Lance Downing, Arnold Milstein, and Li Fei-Fei. 2018. Computer vision-based descriptive analytics of seniors’ daily activities for long-term health monitoring. Machine Learning for Healthcare 2, 1 (2018), 1–18.
[187]
Maxime Lussier, Mélanie Couture, Maxime Moreau, Catherine Laliberté, Sylvain Giroux, Hélène Pigot, Sébastien Gaboury, Kévin Bouchard, Patricia Belchior, Carolina Bottari, Guy Paré, Charles Consel, and Nathalie Bier. 2020. Integrating an ambient assisted living monitoring system into clinical decision-making in home care: An embedded case study. Gerontechnology 19, 1 (2020), 77–92.
[188]
Honghao Lv, Geng Yang, Huiying Zhou, Xiaoyan Huang, Huayong Yang, and Zhibo Pang. 2020. Teleoperation of collaborative robot for remote dementia care in home environments. IEEE Journal of Translational Engineering in Health and Medicine 8 (2020), 1–10.
[189]
Bayard E Lyons, Daniel Austin, Adriana Seelye, Johanna Petersen, Jonathan Yeargers, Thomas Riley, Nicole Sharma, Nora Mattek, Katherine Wild, Hiroko Dodge, and Jeffrey A. Kaye. 2015. Pervasive computing technologies to continuously assess Alzheimer’s disease progression and intervention efficacy. Frontiers in Aging Neuroscience 7 (2015), 102.
[190]
Anzar Mahmood, Nadeem Javaid, and Sohail Razzaq. 2015. A review of wireless communications for smart grid. Renewable and Sustainable Energy Reviews 41 (2015), 248–260.
[191]
Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, and Stefano Vassanelli. 2018. Applications of deep learning and reinforcement learning to biological data. IEEE Transactions on Neural Networks and Learning Systems 29, 6 (2018), 2063–2079.
[192]
Anna Maijala, Hannu Kinnunen, Heli Koskimäki, Timo Jämsä, and Maarit Kangas. 2019. Nocturnal finger skin temperature in menstrual cycle tracking: Ambulatory pilot study using a wearable Oura ring. BMC Women’s Health 19, 1 (2019), 1–10.
[193]
Anindya Majumder. 2004. Power line communications. IEEE Potentials 23, 4 (2004), 4–8.
[194]
Akm Jahangir Majumder, Mohammed Elsaadany, Joshua Aaron Izaguirre, and Donald R. Ucci. 2019. A real-time cardiac monitoring using a multisensory smart IoT system. In Proceedings of the IEEE Annual Computer Software and Applications Conference, Vol. 2, 281–287.
[195]
Anshu Malhotra and Rajni Jindal. 2024. XAI transformer based approach for interpreting depressed and suicidal user behavior on online social networks. Cognitive Systems Research 84 (2024), Article 101186.
[196]
Leandro Y. Mano, Bruno S. Faiçal, Luis H. V. Nakamura, Pedro H. Gomes, Giampaolo L. Libralon, Rodolfo I. Meneguete, P. Rocha Geraldo Filho, Gabriel T. Giancristofaro, Gustavo Pessin, Bhaskar Krishnamachari, and Jó Ueyama. 2016. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Computer Communications 89 (2016), 178–190.
[197]
Sergio Martiradonna, Giulia Cisotto, Gennaro Boggia, Giuseppe Piro, Lorenzo Vangelista, and Stefano Tomasin. 2021. Cascaded WLAN-FWA networking and computing architecture for pervasive in-home healthcare. IEEE Wireless Communications 28, 3 (2021), 92–99.
[198]
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the International Conference on Artificial Intelligence and Statistics, Vol. 54, 1273–1282.
[199]
Javier Medina Quero, Maria Rosa Fernandez Olmo, Maria Dolores Pelaez Aguilera, and Macarena Espinilla Estevez. 2017. Real-time monitoring in home-based cardiac rehabilitation using wrist-worn heart rate devices. Sensors 17, 12 (2017), Article 2892.
[200]
Marci Meingast, Tanya Roosta, and Shankar Sastry. 2006. Security and privacy issues with health care information technology. In Proceedings of the International Conference of Engineering in Medicine and Biology Society. IEEE, 5453–5458.
[201]
Zhaozong Meng, Mingxing Zhang, Changxin Guo, Qirui Fan, Hao Zhang, Nan Gao, and Zonghua Zhang. 2020. Recent progress in sensing and computing techniques for human activity recognition and motion analysis. Electronics 9, 9 (2020), Article 1357.
[202]
Fen Miao, Xurong Wang, Liyan Yin, and Ye Li. 2018. A wearable sensor for arterial stiffness monitoring based on machine learning algorithms. IEEE Sensors Journal 19, 4 (2018), 1426–1434.
[203]
Guanhong Miao, A. Adam Ding, and Samuel S. Wu. 2022. Real-time privacy preserving disease diagnosis using ECG signal. arXiv:2202.03652. Retrieved from https://doi.org/10.48550/arXiv.2202.03652
[204]
Chisaki Miura, Haruhisa Maeda, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda. 2019. Prototyping and preliminary evaluation of mind monitoring service for elderly people at home. In Proceedings of the International Conference on Information Integration and Web-based Applications & Services. ACM, New York, NY, 437–443.
[205]
Chisaki Miura, Haruhisa Maeda, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda. 2020a. Empirical evaluation of mind monitoring service for elderly people at home using LINE chatbot. IEICE Technical Report 119, 477 (2020), 139–144.
[206]
Chisaki Miura, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda. 2020b. Implementing and evaluating feedback feature of mind monitoring service for elderly people at home. In Proceedings of the International Conference on Information Integration and Web-based Applications & Services. ACM, New York, NY, 390–395.
[207]
Safaa Abdullahi Moallim Mohamud, Amin Jalali, and Minho Lee. 2023. Encoder–decoder cycle for visual question answering based on perception-action cycle. Pattern Recognition 144 (2023), Article 109848.
[208]
Mahdiyar Molahasani, Ali Etemad, and Michael Greenspan. 2023a. Continual learning for out-of-distribution pedestrian detection. In Proceedings of the International Conference on Image Processing. IEEE, 2685–2689.
[209]
Mahdiyar Molahasani, Michael Greenspan, and Ali Etemad. 2023b. Can continual learning improve long-tailed recognition? Toward a unified framework. arXiv:2306.13275. Retrieved from https://doi.org/10.48550/arXiv.2306.13275
[210]
Md Sarfaraz Momin, Abu Sufian, Debaditya Barman, Paramartha Dutta, Mianxiong Dong, and Marco Leo. 2022. In-home older adults’ activity pattern monitoring using depth sensors: A review. Sensors 22, 23 (2022), Article 9067.
[211]
Junhyung Moon, Kyoungwoo Lee, and Yong Seung Lee. 2019. Integrated system of monitoring and intervention for in-home healthcare and treatment. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, 393–398.
[212]
Jimmy Moore, Pascal Goffin, Miriah Meyer, Philip Lundrigan, Neal Patwari, Katherine Sward, and Jason Wiese. 2018. Managing in-home environments through sensing, annotating, and visualizing air quality data. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 2, 3 (2018), 1–28.
[213]
Rui Silva Moreira, José Torres, Pedro Sobral, and Christophe Soares. 2019. Intelligent sensing and ubiquitous systems (ISUS) for smarter and safer home healthcare. In Intelligent Pervasive Computing Systems for Smarter Healthcare. Arun Kumar Sangaiah, S. Shantharajah, and Padma Theagarajan (Eds.), Wiley Online Library, 1–36.
[214]
Sajad Abolpour Moshizi, Abolfazl Abedi, Majid Sanaeepur, Christopher J Pastras, Zhao Jun Han, Shuying Wu, and Mohsen Asadnia. 2021. Polymeric piezoresistive airflow sensor to monitor respiratory patterns. Journal of the Royal Society Interface 18, 185 (2021), Article 20210753.
[215]
Vasily Moshnyaga, Tanaka Osamu, Toshin Ryu, and Koji Hashimoto. 2015. Identification of basic behavioral activities by heterogeneous sensors of in-home monitoring system. In Human Behavior Understanding. Gerhard Goos (Ed.), Springer, 160–174.
[216]
Wendy Moyle, Jenny Murfield, and Katarzyna Lion. 2021. The effectiveness of smart home technologies to support the health outcomes of community-dwelling older adults living with dementia: A scoping review. International Journal of Medical Informatics 153 (2021), Article 104513.
[217]
Haider Mshali, Tayeb Lemlouma, and Damien Magoni. 2018. Adaptive monitoring system for e-health smart homes. Pervasive and Mobile Computing 43 (2018), 1–19.
[218]
Muhammad Mubashir, Ling Shao, and Luke Seed. 2013. A survey on fall detection: Principles and approaches. Neurocomputing 100 (2013), 144–152.
[219]
Fadi Muheidat and A Tawalbeh Lo’Ai. 2020. In-home floor-based sensor system-smart carpet-to facilitate healthy aging in place (AIP). IEEE Access 8 (2020), 178627–178638.
[220]
Vincent C. Müller. 2020. Ethics of Artificial Intelligence and Robotics. Stanford University.
[221]
Alvaro Muro-De-La-Herran, Begonya Garcia-Zapirain, and Amaia Mendez-Zorrilla. 2014. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14, 2 (2014), 3362–3394.
[222]
Muddasar Naeem, Giovanni Paragiola, Antonio Coronato, and Giuseppe De Pietro. 2020. A CNN based monitoring system to minimize medication errors during treatment process at home. In Proceedings of the International Conference on Applications of Intelligent Systems. ACM, New York, NY, 1–5.
[223]
Masahide Nakamura, Kenji Hatano, Jun Miyazaki, Kiyoshi Yasuda, Noriaki Kuwahara, Hiroaki Kazui, Sachio Saiki, Seiki Tokunaga, Mihoko Otake, Naoki Kodama, and N. Kosugi. 2019. Developing a system for self-care and mutual-aids of elderly people at home based on externalization of internal states. Grant-in-Aid for Scientific Research 19H01138 (2019), 2019–2023.
[224]
Siddhartha Nambiar, Alexander Nikolaev, Melissa Greene, Lora Cavuoto, and Ann Bisantz. 2016. Low-cost sensor system design for in-home physical activity tracking. IEEE Journal of Translational Engineering in Health and Medicine 4 (2016), 1–6.
[225]
Lucas Medeiros Souza do Nascimento, Lucas Vacilotto Bonfati, Melissa La Banca Freitas, José Jair Alves Mendes Junior, Hugo Valadares Siqueira, and Sergio Luiz Stevan Jr. 2020. Sensors and systems for physical rehabilitation and health monitoring—A review. Sensors 20, 15 (2020), Article 4063.
[226]
Akm Iqtidar Newaz, Amit Kumar Sikder, Mohammad Ashiqur Rahman, and A Selcuk Uluagac. 2021. A survey on security and privacy issues in modern healthcare systems: Attacks and defenses. ACM Transactions on Computing for Healthcare 2, 3 (2021), 1–44.
[227]
Anh Nguyen, Jason Yosinski, and Jeff Clune. 2015. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 427–436.
[228]
Abreha Bayrau Nigusse, Desalegn Alemu Mengistie, Benny Malengier, Granch Berhe Tseghai, and Lieva Van Langenhove. 2021. Wearable smart textiles for long-term electrocardiography monitoring—A review. Sensors 21, 12 (2021), Article 4174.
[229]
Satish Nimmakayala, Bhargav Mummidi, Parvateesam Kunda, and Sanjeev Kumar. 2021. Modern health monitoring system using IoT. In Proceedings of the International Conference on Communications and Cyber Physical Engineering, 1135–1144.
[230]
William S. Noble. 2006. What is a support vector machine? Nature Biotechnology 24, 12 (2006), 1565–1567.
[231]
Fabian Okeke, Emily Tseng, Benedetta Piantella, Mikaela Brown, Harveen Kaur, Madeline R. Sterling, and Nicola Dell. 2019. Technology, home health care, and heart failure: A qualitative analysis with multiple stakeholders. In Proceedings of the ACM Conference on Computing and Sustainable Societies, 122–133.
[232]
O. O. Ogunduyile, K. Zuva, O. A. Randle, and T. Zuva. 2013. Ubiquitous healthcare monitoring system using integrated triaxial accelerometer, Spo2 and location sensors. International Journal of UbiComp 4, 2 (2013), 1–13.
[233]
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. arXiv:1609.03499. Retrieved from https://doi.org/10.48550/arXiv.1609.03499
[234]
Carsten Orwat, Andreas Graefe, and Timm Faulwasser. 2008. Towards pervasive computing in health care–A literature review. Medical Informatics and Decision Making 8, 1 (2008), 1–18.
[235]
Edward J Oughton, William Lehr, Konstantinos Katsaros, Ioannis Selinis, Dean Bubley, and Julius Kusuma. 2021. Revisiting wireless internet connectivity: 5G vs Wi-Fi 6. Telecommunications Policy 45, 5 (2021), Article 102127.
[236]
Koray Ozcan, Senem Velipasalar, and Pramod K. Varshney. 2016. Autonomous fall detection with wearable cameras by using relative entropy distance measure. IEEE Transactions on Human-Machine Systems 47, 1 (2016), 31–39.
[237]
Bruno Pais, Philipp Buluschek, Guillaume DuPasquier, Tobias Nef, Narayan Schütz, Hugo Saner, Daniel Gatica-Perez, and Valérie Santschi. 2020. Evaluation of 1-year in-home monitoring technology by home-dwelling older adults, family caregivers, and nurses. Frontiers in Public Kealth 8 (2020), 538.
[238]
Qiang Pan, Damien Brulin, and Eric Campo. 2021. Home sleep monitoring based on wrist movement data processing. Procedia Computer Science 183 (2021), 696–705.
[239]
Alexandros Pantelopoulos and Nikolaos G. Bourbakis. 2009. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C 40, 1 (2009), 1–12.
[240]
Elena Paraschiv, Cristian Petrache, Ovidiu Bica, and Ana Vasilevschi. 2022. Fall detection system: Continuous in-home monitoring of parkinson’s patients. In Proceedings of the E-Health and Bioengineering Conference. IEEE, 1–4.
[241]
Se Jin Park, Murali Subramaniyam, Seoung Eun Kim, Seunghee Hong, Joo Hyeong Lee, Chan Min Jo, and Youngseob Seo. 2017. Development of the elderly healthcare monitoring system with IoT. In Advances in Human Factors and Ergonomics in Healthcare. Janusz Kacprzyk (Ed.), Springer, 309–315.
[242]
Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2013. How to construct deep recurrent neural networks. arXiv:1312.6026. Retrieved from https://doi.org/10.48550/arXiv.1312.6026
[243]
Sushant Kumar Pattnaik, Soumya Ranjan Samal, Shuvabrata Bandopadhaya, Kaliprasanna Swain, Subhashree Choudhury, Jitendra Kumar Das, Albena Mihovska, and Vladimir Poulkov. 2022. Future wireless communication technology towards 6g IoT: an application-based analysis of IoT in real-time location monitoring of employees inside underground mines by using BLE. Sensors 22, 9 (2022), Article 3438.
[244]
Stephen D. Persell, Kunal N. Karmali, Natalie Stein, Jim Li, Yaw A. Peprah, Dawid Lipiszko, Jody D. Ciolino, and Hironori Sato. 2018. Design of a randomized controlled trial comparing a mobile phone-based hypertension health coaching application to home blood pressure monitoring alone: the Smart Hypertension Control Study. Contemporary Clinical Trials 73 (2018), 92–97.
[245]
Minh Pham, Yehenew Mengistu, Ha Do, and Weihua Sheng. 2018. Delivering home healthcare through a cloud-based smart home environment. Future Generation Computer Systems 81 (2018), 129–140.
[246]
Nada Y. Philip, Joel J. P. C. Rodrigues, Honggang Wang, Simon James Fong, and Jia Chen. 2021. Internet of Things for in-home health monitoring systems: Current advances, challenges and future directions. IEEE Journal on Selected Areas in Communications 39, 2 (2021), 300–310.
[247]
Ming-Zher Poh, Tobias Loddenkemper, Claus Reinsberger, Nicholas C. Swenson, Shubhi Goyal, Mangwe C. Sabtala, Joseph R. Madsen, and Rosalind W. Picard. 2012. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia 53, 5 (2012), e93–e97.
[248]
Sudhir Ponnapalli, Raghunadha Reddi Dornala, and S. Parvathi Vallabaneni. 2023. A triple-tap hybrid load balancing system (TTHLB) for health monitoring system. In Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud. IEEE, 616–622.
[249]
Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xi-Zhao Wang, and Q. M. Jonathan Wu. 2023a. A review of generalized zero-shot learning methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 4 (2023), 4051–4070.
[250]
Farhad Pourpanah, Chee Peng Lim, Ali Etemad, and Q. M. Jonathan Wu. 2023b. An ensemble semi-supervised adaptive resonance theory model with explanation capability for pattern classification. IEEE Transactions on Emerging Topics in Computational Intelligence 8, 1 (2023), 1–14.
[251]
Farhad Pourpanah, Chee Peng Lim, and Qi Hao. 2019a. A reinforced fuzzy ARTMAP model for data classification. International Journal of Machine Learning and Cybernetics 10 (2019), 1643–1655.
[252]
Farhad Pourpanah, Di Wang, Ran Wang, and Chee Peng Lim. 2021. A semisupervised learning model based on fuzzy min–max neural networks for data classification. Applied Soft Computing 112 (2021), Article 107856.
[253]
Farhad Pourpanah, Ran Wang, Chee Peng Lim, Xizhao Wang, Manjeevan Seera, and Choo Jun Tan. 2019b. An improved fuzzy ARTMAP and Q-learning agent model for pattern classification. Neurocomputing 359 (2019), 139–152.
[254]
Farhad Pourpanah, Bin Zhang, Rui Ma, and Qi Hao. 2018. Non-intrusive human motion recognition using distributed sparse sensors and the genetic algorithm based neural network. In Proceedings of the IEEE Sensors, 1–4.
[255]
Peter J. Pronovost, Melissa D. Cole, and Robert M. Hughes. 2022. Remote patient monitoring during COVID-19: An unexpected patient safety benefit. Jama 327, 12 (2022), 1125–1126.
[256]
Juha Puustjärvi and Leena Puustjärvi. 2015. The role of smart data in smart home: health monitoring case. Procedia Computer Science 69 (2015), 143–151.
[257]
Sen Qiu, Zhelong Wang, Hongyu Zhao, Kairong Qin, Zhenglin Li, and Huosheng Hu. 2018. Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion. Information Fusion 39 (2018), 108–119.
[258]
Meheroz H. Rabadi. 2021. Fever in a paraplegia patient with a pressure ulcer. Radiology Case Reports 16, 9 (2021), 2434–2436.
[259]
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
[260]
Setareh Rahimi Taghanaki, Michael J. Rainbow, and Ali Etemad. 2021. Self-supervised human activity recognition by learning to predict cross-dimensional motion. In Proceedings of the International Symposium on Wearable Computers. ACM, New York, NY, 23–27.
[261]
Md Abdur Rahman and M. Shamim Hossain. 2016. A gesture-based smart home-oriented health monitoring service for people with physical impairments. In Inclusive Smart Cities and Digital Health. Gerhard Goos (Ed.), Springer, 464–476.
[262]
Md Abdur Rahman and M. Shamim Hossain. 2017. m-Therapy: A multisensor framework for in-home therapy management: A social therapy of things perspective. IEEE Internet of Things Journal 5, 4 (2017), 2548–2556.
[263]
Md Abdur Rahman and M. Shamim Hossain. 2021. An internet-of-medical-things-enabled edge computing framework for tackling COVID-19. IEEE Internet of Things Journal 8, 21 (2021), 15847–15854.
[264]
Sandeep Raj. 2020. An efficient IoT-based platform for remote real-time cardiac activity monitoring. IEEE Transactions on Consumer Electronics 66, 2 (2020), 106–114.
[265]
Rajkumar Rajasekaran, K. Govinda, Jolly Masih, and M. Sruthi. 2020. Health monitoring system for individuals using internet of things. In Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, IGI Global, 150–164.
[266]
Roger Ratcliff. 1978. A theory of memory retrieval. Psychological Review 85, 2 (1978), 59.
[267]
Waseem Rawat and Zenghui Wang. 2017. Deep convolutional neural networks for image classification: A comprehensive review. Neural computation 29, 9 (2017), 2352–2449.
[268]
Usman Raza, Parag Kulkarni, and Mahesh Sooriyabandara. 2017. Low power wide area networks: An overview. IEEE Communications Surveys & Tutorials 19, 2 (2017), 855–873.
[269]
Anu Radha Reddy, G. S. Pradeep Ghantasala, Rizwan Patan, R. Manikandan, and Suresh Kallam. 2021. Smart assistance of elderly individuals in emergency situations at home. In Internet of Medical Things: Remote Healthcare Systems and Applications. Giancarlo Fortino, and Antonio Liotta (Eds.), Springer, 95–115.
[270]
Sai Deepika Regani, Yuqian Hu, Beibei Wang, and KJ Ray Liu. 2022. Wifi-based robust indoor localization for daily activity monitoring. In Proceedings of the ACM Workshop on Mobile and Wireless Sensing for Smart Healthcare, 1–6.
[271]
Danilo Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. In International Conference on Machine Learning. Proceedings of Machine Learning Research, 1530–1538.
[272]
Mary M. Rodgers, Vinay M. Pai, and Richard S. Conroy. 2014. Recent advances in wearable sensors for health monitoring. IEEE Sensors Journal 15, 6 (2014), 3119–3126.
[273]
Kyle Ross, Paul Hungler, and Ali Etemad. 2021. Unsupervised multi-modal representation learning for affective computing with multi-corpus wearable data. Journal of Ambient Intelligence and Humanized Computing 14 (2021), 1–26.
[274]
Nils Roth, Christine F. Martindale, Bjoern M. Eskofier, Heiko Gaßner, Zacharias Kohl, and Jochen Klucken. 2018. Synchronized sensor insoles for clinical gait analysis in home-monitoring applications. Current Directions in Biomedical Engineering 4, 1 (2018), 433–437.
[275]
Catherine Rowan and Robert Hirten. 2022. The future of telemedicine and wearable technology in IBD. Current Opinion in Gastroenterology 38, 4 (2022), 373–381.
[276]
Nicole Ruggiano, Ellen L. Brown, Lisa Roberts, C. Victoria Framil Suarez, Yan Luo, Zhichao Hao, and Vagelis Hristidis. 2021. Chatbots to support people with dementia and their caregivers: systematic review of functions and quality. Journal of Medical Internet Research 23 (2021), e25006.
[277]
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986. Learning representations by back-propagating errors. Nature 323, 6088 (1986), 533–536.
[278]
Ibrahim Sadek and Mounir Mohktari. 2018. Nonintrusive remote monitoring of sleep in home-based situation. Journal of Medical Systems 42, 4 (2018), 64.
[279]
Daniel Sanchez-Morillo, Miguel A. Fernandez-Granero, and Antonio Leon-Jimenez. 2016. Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: a systematic review. Chronic Respiratory Disease 13, 3 (2016), 264–283.
[280]
Quentin Sanders, Vicky Chan, Renee Augsburger, Steven C. Cramer, David J. Reinkensmeyer, and An H. Do. 2020. Feasibility of wearable sensing for in-home finger rehabilitation early after stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, 6 (2020), 1363–1372.
[281]
Pritam Sarkar and Ali Etemad. 2020. Self-supervised ECG representation learning for emotion recognition. IEEE Transactions on Affective Computing 13, 3 (2020), 1541–1554.
[282]
Pritam Sarkar and Ali Etemad. 2021. CardioGan: Attentive generative adversarial network with dual discriminators for synthesis of ECG from PPG. In Proceedings of the Association for the Advancement of Artificial Intelligence, Vol. 35, 488–496.
[283]
Pritam Sarkar, Silvia Lobmaier, Bibiana Fabre, Diego González, Alexander Mueller, Martin G. Frasch, Marta C. Antonelli, and Ali Etemad. 2021. Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning. Scientific Reports 11, 1 (2021), 1–10.
[284]
Michael J. Sateia. 2014. International classification of sleep disorders. Chest 146, 5 (2014), 1387–1394.
[285]
Dominik Scherer, Andreas Müller, and Sven Behnke. 2010. Evaluation of pooling operations in convolutional architectures for object recognition. In Proceedings of the International Conference on Artificial Neural Networks, 92–101.
[286]
Udo Schlegel, Hiba Arnout, Mennatallah El-Assady, Daniela Oelke, and Daniel A. Keim. 2019. Towards a rigorous evaluation of XAI methods on time series. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop, 4197–4201.
[287]
Narayan Schütz, Hugo Saner, Angela Botros, Philipp Buluschek, Prabitha Urwyler, René M. Müri, and Tobias Nef. 2021. Wearable based calibration of contactless in-home motion sensors for physical activity monitoring in community-dwelling older adults. Frontiers in Digital Health 2 (2021), Article 566595.
[288]
Paul Scovanner, Saad Ali, and Mubarak Shah. 2007. A 3-dimensional sift descriptor and its application to action recognition. In Proceedings of the ACM International Conference on Multimedia, 357–360.
[289]
Valeria Sebri and Lucrezia Savioni. 2020. An introduction to personalized eHealth. In P5 eHealth: An Agenda for the Health Technologies of the Future. Gabriella Pravettoni, and Stefano Triberti (Eds.), Springer, 53.
[290]
Ann-Kathrin Seifert, Moeness G. Amin, and Abdelhak M. Zoubir. 2019. Toward unobtrusive in-home gait analysis based on radar micro-Doppler signatures. IEEE Transactions on Biomedical Engineering 66, 9 (2019), 2629–2640.
[291]
Alireza Sepas-Moghaddam and Ali Etemad. 2022. Deep gait recognition: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 1 (2022), 264–284.
[292]
Alireza Sepas-Moghaddam, Ali Etemad, Paulo Lobato Correia, and Fernando Pereira. 2019. A deep framework for facial emotion recognition using light field images. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction. IEEE, 1–7.
[293]
Muhammad Shabaan, Kaleem Arshid, Muhammad Yaqub, Feng Jinchao, M. Sultan Zia, Giridhar Reddy Bojja, Muazzam Iftikhar, Usman Ghani, Loknath Sai Ambati, and Rizwan Munir. 2020. Survey: Smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis. Medical Informatics and Decision Making 20 (2020), 1–16.
[294]
Kyarash Shahriari and Mana Shahriari. 2017. IEEE standard review—Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems. In Proceedings of the IEEE Canada International Humanitarian Technology Conference, 197–201.
[295]
Praveer Sharan. 2022. Automated discrimination of cough in audio recordings: A scoping review. Frontiers in Signal Processing 2 (2022), Article 759684.
[296]
Lakhan Dev Sharma, Vijay Kumar Bohat, Maria Habib, Al-Zoubi Ala’M, Hossam Faris, and Ibrahim Aljarah. 2022. Evolutionary inspired approach for mental stress detection using EEG signal. Expert Systems with Applications 197 (2022), Article 116634.
[297]
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge computing: Vision and challenges. IEEE Internet of Things Journal 3, 5 (2016), 637–646.
[298]
Debaditya Shome, Pritam Sarkar, and Ali Etemad. 2024. Region-disentangled diffusion model for high-fidelity PPG-to-ECG translation. Proceedings of the Association for the Advancement of Artificial Intelligence, 15009–15019.
[299]
Ali I. Siam, Mohammed A. El-Affendi, Atef Abou Elazm, Ghada M. El-Banby, Nirmeen A. El-Bahnasawy, Fathi E. Abd El-Samie, and Ahmed A Abd El-Latif. 2023. Portable and real-time IoT-based healthcare monitoring system for daily medical applications. IEEE Transactions on Computational Social Systems 10, 4 (2023), 1629–1641.
[300]
Erika Aparecida Silveira, Larissa Silva Barbosa, Ana Paula Santos Rodrigues, Matias Noll, and Cesar De Oliveira. 2020. Body fat percentage assessment by skinfold equation, bioimpedance and densitometry in older adults. Archives of Public Health 78, 1 (2020), 1–9.
[301]
Anuradha Singh, Saeed Ur Rehman, Sira Yongchareon, and Peter Han Joo Chong. 2020. Sensor technologies for fall detection systems: A review. IEEE Sensors Journal 20, 13 (2020), 6889–6919.
[302]
Marjorie Skubic, Rainer Dane Guevara, and Marilyn Rantz. 2015. Automated health alerts using in-home sensor data for embedded health assessment. IEEE Iournal of Translational Engineering in Health and Medicine 3 (2015), 1–11.
[303]
Sahar Soltanieh, Ali Etemad, and Javad Hashemi. 2022. Analysis of augmentations for contrastive ECG representation learning. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1–10.
[304]
Sahar Soltanieh, Javad Hashemi, and Ali Etemad. 2023. In-distribution and out-of-distribution self-supervised ECG representation learning for arrhythmia detection. IEEE Journal of Biomedical and Health Informatics 28, 2 (2023), 789–800.
[305]
Haiba Somaya and Mazri Tomadar. 2019. Secure communication in E-health care system monitoring. In Proceedings of the International Conference on Smart City Applications. ACM, New York, NY, 1–9.
[306]
Gautam Srivastava, Ashutosh Dhar Dwivedi, and Rajani Singh. 2018. Automated remote patient monitoring: Data sharing and privacy using blockchain. arXiv:1811.03417. Retrieved from https://doi.org/10.48550/arXiv.1811.03417
[307]
James H. Stapleton. 2009. Linear Statistical Models. Vol. 719. John Wiley & Sons.
[308]
Avln Sujith, Guna Sekhar Sajja, V. Mahalakshmi, Shibili Nuhmani, and B. Prasanalakshmi. 2022. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neuroscience Informatics 2, 3 (2022), Article 100028.
[309]
Alysa Ziying Tan, Han Yu, Lizhen Cui, and Qiang Yang. 2022. Towards personalized federated learning. IEEE Transactions on Neural Networks and Learning Systems 34, 12 (2022), 9587–9603.
[310]
Savas Tasoglu. 2022. Toilet-based continuous health monitoring using urine. Nature Reviews Urology 19, 4 (2022), 219–230.
[311]
A. Dharma Teja and K. Srihari Rao. 2018. A smart wearable system for ECG and health monitoring. International Journal for Advance Research and Development 3, 2 (2018), 57–63.
[312]
Nicole A. Thomas, Anna Drewry, Susan Racine Passmore, Nadia Assad, and Kara K. Hoppe. 2021. Patient perceptions, opinions and satisfaction of telehealth with remote blood pressure monitoring postpartum. Pregnancy and Childbirth 21 (2021), 1–11.
[313]
Marita G. Titler. 2008. The evidence for evidence-based practice implementation. In Patient Safety and Quality: An Evidence-based Handbook for Nurses. Hughes R.G. (Ed.).
[314]
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. 2021. Training data-efficient image transformers & distillation through attention. In Proceedings of the International Conference on Machine Learning. Proceedings of Machine Learning Research, 10347–10357.
[315]
Bang Tran, Sai Harshavardhan Reddy Kona, Xiaohui Liang, Gabriel Ghinita, Caroline Summerour, and John A. Batsis. 2023. VPASS: Voice privacy assistant system for monitoring in-home voice commands. In Proceedings of the Annual International Conference on Privacy, Security and Trust, 1–10.
[316]
Michael Tschannen, Olivier Bachem, and Mario Lucic. 2018. Recent advances in autoencoder-based representation learning. arXiv:1812.05069. Retrieved from https://doi.org/10.48550/arXiv.1812.05069
[317]
Toshifumi Tsukiyama. 2015. In-home health monitoring system for solitary elderly. Procedia Computer Science 63 (2015), 229–235.
[318]
Md Zia Uddin, Mohammed Mehedi Hassan, Ahmed Alsanad, and Claudio Savaglio. 2020. A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Information Fusion 55 (2020), 105–115.
[319]
Muhammad Usman, James Rains, Tie Jun Cui, Muhammad Zakir Khan, Jalil Ur Rehman Kazim, Muhammad Ali Imran, and Qammer H. Abbasi. 2022. Intelligent wireless walls for contactless in-home monitoring. Light: Science & Applications 11, 1 (2022), 212.
[320]
Anthony Ngozichukwuka Uwaechia and Nor Muzlifah Mahyuddin. 2020. A comprehensive survey on millimeter wave communications for fifth-generation wireless networks: Feasibility and challenges. IEEE Access 8 (2020), 62367–62414.
[321]
Thavavel Vaiyapuri, E. Laxmi Lydia, Mohamed Yacin Sikkandar, Vicente García Díaz, Irina V. Pustokhina, and Denis A. Pustokhin. 2021. Internet of things and deep learning enabled elderly fall detection model for smart homecare. IEEE Access 9 (2021), 113879–113888.
[322]
Jesper E. Van Engelen and Holger H. Hoos. 2020. A survey on semi-supervised learning. Machine Learning 109, 2 (2020), 373–440.
[323]
Bert Vandenberghe and David Geerts. 2015. Sleep monitoring tools at home and in the hospital: Bridging quantified self and clinical sleep research. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare. ACM, New York, NY, 153–160.
[324]
C. VandeWeerd, A. Yalcin, G. Aden-Buie, Y. Wang, M. Roberts, N. Mahser, C. Fnu, and D. Fabiano. 2020. HomeSense: Design of an ambient home health and wellness monitoring platform for older adults. Health and Technology 10 (2020), 1291–1309.
[325]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 30, 1–11.
[326]
Prabal Verma and Sandeep K. Sood. 2018. Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet of Things Journal 5, 3 (2018), 1789–1796.
[327]
Chalavadi Vishnu, Rajeshreddy Datla, Debaditya Roy, Sobhan Babu, and C. Krishna Mohan. 2021. Human fall detection in surveillance videos using fall motion vector modeling. IEEE Sensors Journal 21, 15 (2021), 17162–17170.
[328]
JinFeng Wang, ZhenYu He, ShuaiHui Huang, Hao Chen, WenZhong Wang, and Farhad Pourpanah. 2021a. Fuzzy measure with regularization for gene selection and cancer prediction. International Journal of Machine Learning and Cybernetics 12 (2021), 2389–2405.
[329]
Ju Wang, Nicolai Spicher, Joana M. Warnecke, Mostafa Haghi, Jonas Schwartze, and Thomas M. Deserno. 2021b. Unobtrusive health monitoring in private spaces: The smart home. Sensors 21, 3 (2021), 864.
[330]
Ju Wang, Jing Wang, Hongyu Miao, Michael Marschollek, Klaus-Hendrik Wolf, Kerry A. Lynch, and Yang Gong. 2018. Leveraging aging in place through sensor-enhanced in-home monitoring. In Nursing Informatics, A. K. Rotegård, D. J. Skiba, S. Barbosa, and A. G. Davalos Alcázar (Eds.), IOS Press Ebooks, 19–23.
[331]
Jiayuan Wang, Q. M. Jonathan Wu, and Farhad Pourpanah. 2023b. An attentive-based generative model for medical image synthesis. International Journal of Machine Learning and Cybernetics 14, 11 (2023), 3897–3910.
[332]
Jiayuan Wang, Q. M. Jonathan Wu, and Farhad Pourpanah. 2023c. Dc-cycleGAN: bidirectional CT-to-MR synthesis from unpaired data. Computerized Medical Imaging and Graphics 108 (2023), Article 102249.
[333]
Ke Wang, Tingting Song, Yitong Wang, Chengwei Fang, Jiayuan He, Ampalavanapillai Nirmalathas, Christina Lim, Elaine Wong, and Sithamparanathan Kandeepan. 2022. Evolution of short-range optical wireless communications. Journal of Lightwave Technology 41, 4 (2022), 1019–1040.
[334]
Tinghui Wang and Diane J Cook. 2021. Multi-person activity recognition in continuously monitored smart homes. IEEE Transactions on Emerging Topics in Computing 10, 2 (2021), 1130–1141.
[335]
Xizhao Wang, Yanxia Zhao, and Farhad Pourpanah. 2020. Recent advances in deep learning. International Journal of Machine Learning and Cybernetics 11 (2020), 747–750.
[336]
Zhi Wang, Beihong Jin, Siheng Li, Fusang Zhang, and Wenbo Zhang. 2023a. ECG-grained cardiac monitoring using UWB signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1–25.
[337]
N. Watthanawisuth, T. Lomas, A. Wisitsoraat, and A. Tuantranont. 2010. Wireless wearable pulse oximeter for health monitoring using ZigBee wireless sensor network. In Proceedings of the International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. IEEE, 575–579.
[338]
Mark Weiser. 1999. The computer for the 21st century. ACM Mobile Computing and Communications Review 3, 3 (1999), 3–11.
[339]
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. 2022. Transformers in time series: A survey. arXiv:2202.07125. Retrieved from https://doi.org/10.48550/arXiv.2202.07125
[340]
Thomas Wiatowski and Helmut Bölcskei. 2018. A mathematical theory of deep convolutional neural networks for feature extraction. IEEE Transactions on Information Theory 64, 3 (2018), 1845–1866.
[341]
Marlies S. Wijsenbeek, Catharina C. Moor, Kerri A. Johannson, Peter D. Jackson, Yet H. Khor, Yasuhiro Kondoh, Sujeet K. Rajan, Gabriela C. Tabaj, Brenda E. Varela, Pieter van der Wal, Richard N van Zyl-Smit, Michael Kreuter, and Toby M. Maher. 2023. Home monitoring in interstitial lung diseases. The Lancet Respiratory Medicine 11, 1 (2023), 97–110.
[342]
Peter J. Winzer. 2010. Beyond 100G ethernet. IEEE Communications Magazine 48, 7 (2010), 26–30.
[343]
Oliver J. Woodman. 2007. An Introduction to Inertial Navigation. Technical Report. University of Cambridge, Computer Laboratory.
[344]
Qiong Wu, Xu Chen, Zhi Zhou, and Junshan Zhang. 2020. FedHome: Cloud-edge based personalized federated learning for in-home health monitoring. IEEE Transactions on Mobile Computing 21, 8 (2020), 2818–2832.
[345]
Wenlong Wu, James M. Keller, Marjorie Skubic, and Mihail Popescu. 2019. Data stream trajectory analysis using sequential possibilistic Gaussian mixture model. In Proceedings of the IEEE International Conference on Fuzzy Systems, 1–7.
[346]
Wenlong Wu, James M. Keller, Marjorie Skubic, Mihail Popescu, and Kari R. Lane. 2021. Early detection of health changes in the elderly using in-home multi-sensor data streams. ACM Transactions on Computing for Healthcare 2, 3 (2021), 1–23.
[347]
Jiali Xie, Pedro Fonseca, Johannes van Dijk, Sebastiaan Overeem, and Xi Long. 2023. Assessment of obstructive sleep apnea severity using audio-based snoring features. Biomedical Signal Processing and Control 86 (2023), Article 104942.
[348]
Zongxing Xie, Bing Zhou, Xi Cheng, Elinor Schoenfeld, and Fan Ye. 2022. Passive and context-aware in-home vital signs monitoring using co-located UWB-depth sensor fusion. ACM Transactions on Computing for Healthcare 3, 4 (2022), 1–31.
[349]
Shudong Yang, Xueying Yu, and Ying Zhou. 2020. LSTM and GRU neural network performance comparison study: Taking yelp review dataset as an example. In Proceedings of the International Workshop on Electronic Communication and Artificial Intelligence. IEEE, 98–101.
[350]
Ziqi Yang, Xuhai Xu, Bingsheng Yao, Shao Zhang, Ethan Rogers, Stephen Intille, Nawar Shara, and Dakuo Wang. 2023. Talk2Care: Facilitating asynchronous patient-provider communication with large-language-model. arXiv:2309.09357. Retrieved from https://doi.org/10.48550/arXiv.2309.09357
[351]
Jingyu Yu, Ning An, Tanbir Hassan, and Quan Kong. 2019a. A pilot study on a smart home for elders based on continuous in-home unobtrusive monitoring technology. Health Environments Research & Design Journal 12, 3 (2019), 206–219.
[352]
Yong Yu, Xiaosheng Si, Changhua Hu, and Jianxun Zhang. 2019b. A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation 31, 7 (2019), 1235–1270.
[353]
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. 2022. Ts2vec: Towards universal representation of time series. In Proceedings of the Association for the Advancement of Artificial Intelligence, Vol. 36, 8980–8987.
[354]
Yin Yuehong, Yan Zeng, Xing Chen, and Yuanjie Fan. 2016. The internet of things in healthcare: An overview. Journal of Industrial Information Integration 1 (2016), 3–13.
[355]
Olivia Yvellez, Michael J. Andersen, Maya Aharoni Golan, Dylan M. Rodriquez, Nada Zmeter, Katia El Jurdi, and David T. Rubin. 2018. P051 IBD patient compliance with mobile and wearable technologies as tools to assess quality of life, sleep quality and pain. Gastroenterology 154, 1 (2018), S26–S27.
[356]
Faheem Zafari, Athanasios Gkelias, and Kin K. Leung. 2019. A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials 21, 3 (2019), 2568–2599.
[357]
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed. 2020. Big bird: Transformers for longer sequences. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 17283–17297.
[358]
Guangyi Zhang, Vandad Davoodnia, and Ali Etemad. 2022. Parse: Pairwise alignment of representations in semi-supervised eeg learning for emotion recognition. IEEE Transactions on Affective Computing 13, 4 (2022), 2185–2200.
[359]
Guangyi Zhang, Vandad Davoodnia, Alireza Sepas-Moghaddam, Yaoxue Zhang, and Ali Etemad. 2019. Classification of hand movements from EEG using a deep attention-based LSTM network. IEEE Sensors Journal 20, 6 (2019), 3113–3122.
[360]
G. Zhang and A. Etemad. 2021. Deep recurrent semi-supervised EEG representation learning for emotion recognition. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction. IEEE Computer Society, 1–8.
[361]
Mingyang Zhang, Yingyou Wen, Jian Chen, Xiaotao Yang, Rui Gao, and Hong Zhao. 2018. Pedestrian dead-reckoning indoor localization based on OS-ELM. IEEE Access 6 (2018), 6116–6129.
[362]
Shigeng Zhang, Xuan Liu, Yangyang Liu, Bo Ding, Song Guo, and Jianxin Wang. 2020a. Accurate respiration monitoring for mobile users with commercial RFID devices. IEEE Journal on Selected Areas in Communications 39, 2 (2020), 513–525.
[363]
Xuewan Zhang, Liuqing Yang, Zhiguo Ding, Jian Song, Yunkai Zhai, and Di Zhang. 2020b. Sparse vector coding-based multi-carrier NOMA for in-home health networks. IEEE Journal on Selected Areas in Communications 39, 2 (2020), 325–337.
[364]
Yu Zhang, Peter Tiňo, Aleš Leonardis, and Ke Tang. 2021. A survey on neural network interpretability. IEEE Transactions on Emerging Topics in Computational Intelligence 5, 5 (2021), 726–742.
[365]
Feng Zhao, Zhiguo Cao, Yang Xiao, Jing Mao, and Junsong Yuan. 2018. Real-time detection of fall from bed using a single depth camera. IEEE Transactions on Automation Science and Engineering 16, 3 (2018), 1018–1032.
[366]
Yifan Zhao, Jiaxin Wang, Yifei Zhang, Hejian Liu, Zi’ang Chen, Yujiao Lu, Yanning Dai, Lijun Xu, and Shuo Gao. 2021. Flexible and wearable EMG and PSD sensors enabled locomotion mode recognition for IoHT-based in-home rehabilitation. IEEE Sensors Journal 21, 23 (2021), 26311–26319.
[367]
Xinlei Zhou, Han Liu, Farhad Pourpanah, Tieyong Zeng, and Xizhao Wang. 2022. A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications. Neurocomputing 489 (2022), 449–465.
[368]
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, and Kostas Daniilidis. 2019. Unsupervised event-based learning of optical flow, depth, and egomotion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 989–997.
[369]
Zhihua Zhu, Tao Liu, Guangyi Li, Tong Li, and Yoshio Inoue. 2015. Wearable sensor systems for infants. Sensors 15, 2 (2015), 3721–3749.

Cited By

View all
  • (2024)EEG Transmission Over 6G Using Optimized Pilot-Based Channel Estimation2024 5th International Conference on Circuits, Control, Communication and Computing (I4C)10.1109/I4C62240.2024.10748522(409-414)Online publication date: 4-Oct-2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Computing for Healthcare
ACM Transactions on Computing for Healthcare  Volume 5, Issue 4
October 2024
195 pages
EISSN:2637-8051
DOI:10.1145/3613740
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2024
Online AM: 19 July 2024
Accepted: 31 May 2024
Revised: 16 February 2024
Received: 20 June 2023
Published in HEALTH Volume 5, Issue 4

Check for updates

Author Tags

  1. In-home monitoring
  2. healthcare
  3. ubiquitous computing
  4. ambient intelligence
  5. sensor technologies
  6. Deep learning
  7. internet of things

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)424
  • Downloads (Last 6 weeks)137
Reflects downloads up to 25 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)EEG Transmission Over 6G Using Optimized Pilot-Based Channel Estimation2024 5th International Conference on Circuits, Control, Communication and Computing (I4C)10.1109/I4C62240.2024.10748522(409-414)Online publication date: 4-Oct-2024

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media