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

Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living

Published: 04 January 2021 Publication History
  • Get Citation Alerts
  • Abstract

    Wearable systems have unlocked new sensing paradigms in various applications such as human activity recognition, which can enhance effectiveness of mobile health applications. Current systems using wearables are not capable of understanding their surroundings, which limits their sensing capabilities. For instance, distinguishing certain activities such as attending a meeting or class, which have similar motion patterns but happen in different contexts, is challenging by merely using wearable motion sensors. This article focuses on understanding user's surroundings, i.e., environmental context, to enhance capability of wearables, with focus on detecting complex activities of daily living (ADL). We develop a methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale. This system does not require specific infrastructure or additional hardware. We develop a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. The proposed system contains a general ADL classifier working with motion sensors, learns personalized context, and uses that to reduce the search space of activities to those that occur within a certain context. We collected real-world data of complex ADLs and by narrowing the search space with context, we improve average F1-score from 0.72 to 0.80.

    References

    [1]
    A. Akbari and R. Jafari. 2019. Transferring activity recognition models for new wearable sensors with deep generative domain adaptation. In Proceedings of the 18th International Conference on Information Processing in Sensor Networks 2019, 85--96.
    [2]
    B. Ibrahim, J. McMurray, and R. Jafari. 2018. A wrist-worn strap with an array of electrodes for robust physiological sensing. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’18). 4313--4317.
    [3]
    M. Esfahani, M. Iman, and M. A. Nussbaum. 2019. Classifying diverse physical activities using smart garments. Sensors 19, 14 (2019), 3133.
    [4]
    P. Bharti, D. De, S. Chellappan, and S. K. Das. 2019. HuMAn: Complex activity recognition with multi-modal multi-positional body sensing. IEEE Trans. Mob. Comput. 18, 4 (2019), 857--870.
    [5]
    R. Solis, A. Pakbin, A. Akbari, B. J. Mortazavi, and R. Jafari. 2019. A human-centered wearable sensing platform with intelligent automated data annotation capabilities. In Proceedings of the International Conference on Internet of Things Design and Implementation 2019, 255--260.
    [6]
    G. Sprint, D. Cook, R. Fritz, and M. Schmitter-Edgecombe. 2016. Detecting health and behavior change by analyzing smart home sensor data. In Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP’16). 1--3.
    [7]
    P. Bharti, A. Panwar, G. Gopalakrishna, and S. Chellappan. 2017. Watch-dog: Detecting self-harming activities from wrist worn accelerometers. IEEE J. Biomed. Heal. Inf. 22, 3 (2017), 686--696.
    [8]
    L. Wang, T. Gu, X. Tao, H. Chen, and J. Lu. 2011. Recognizing multi-user activities using wearable sensors in a smart home. Perv Mob. Comput 7, 3 (2011), 287--298.
    [9]
    A. Bulling, U. Blanke, and B. Schiele. 2014. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv 46, 3 (2014), 33.
    [10]
    A. Akbari, J. Wu, R. Grimsley, and R. Jafari. 2018. Hierarchical signal segmentation and classification for accurate activity recognition. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 1596--1605.
    [11]
    A. Akbari and R. Jafari. 2019. An autoencoder-based approach for recognizing null class in activities of daily living in-the-wild via wearable motion sensors. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19). 3392--3396.
    [12]
    J. C. Krumm, E. J. Horvitz, and R. Hariharan. 2011. Integration of location logs, GPS signals, and spatial resources for identifying user activities, goals, and context. Google Patents, 2011.
    [13]
    E. R. Sykes, S. Pentland, and S. Nardi. 2015. Context-aware mobile apps using iBeacons: Towards smarter interactions. In Proceedings of the 25th Annual International Conference on Computer Science and Software Engineering. 120--129.
    [14]
    G. W. Musumba and H. O. Nyongesa. 2013. Context awareness in mobile computing: A review. Int. J. Mach. Learn. Appl. 2, 1 (2013), 5.
    [15]
    M. Weiser. 1991. The computer for the 21st century IEEE Perv. Comput. 265, 3 (1991), 94--105.
    [16]
    C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos. 2014. Context aware computing for the internet of things: A survey. IEEE Commun. Surv. Tutor. 16, 1 (2014), 414--454.
    [17]
    L. Herranz, S. Jiang, and R. Xu. 2016. Modeling restaurant context for food recognition. IEEE Trans. Multimed. 19, 2 (2016), 430--440.
    [18]
    R. Reichle et al. 2008. A comprehensive context modeling framework for pervasive computing systems. In Proceedings of the IFIP International Conference on Distributed Applications and Interoperable Systems. 281--295.
    [19]
    D. De, P. Bharti, S. K. Das, and S. Chellappan. 2015. Multimodal wearable sensing for fine-grained activity recognition in healthcare. IEEE Internet Comput. 19, 5 (2015), 26--35.
    [20]
    A. Filippoupolitis, W. Oliff, B. Takand, and G. Loukas. 2017. Location-enhanced activity recognition in indoor environments using off the shelf smart watch technology and BLE beacons. Sensors 17, 6 (2017), 1230.
    [21]
    L. Aalto, N. Göthlin, J. Korhonen, and T. Ojala. 2004. Bluetooth and WAP push based location-aware mobile advertising system. In Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services. 49--58.
    [22]
    J. Zheng and L. M. Ni. 2013. An unsupervised learning approach to social circles detection in ego bluetooth proximity network. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 721--724.
    [23]
    M. Han, J. Bang, C. Nugent, S. McClean, and S. Lee. 2014. A lightweight hierarchical activity recognition framework using smartphone sensors. Sensors 14, 9 (2014), 16181--16195.
    [24]
    A. Subramanya, A. Raj, J. A. Bilmes, and D. Fox. 2012. Recognizing activities and spatial context using wearable sensors. arXiv:1206.6869. Retrieved from https://arxiv.org/abs/1206.6869.
    [25]
    J. Chon and H. Cha. 2011. Lifemap: A smartphone-based context provider for location-based services. IEEE Perv. Comput. 10, 2 (2011), 58--67.
    [26]
    N. Bulusu, J. Heidemann, D. Estrin, and others. 2000. GPS-less low-cost outdoor localization for very small devices. IEEE Pers. Commun. 7, 5 (2000), 28--34.
    [27]
    T.-B. Nguyen, T. Nguyen, W. Luo, S. Venkatesh, and D. Phung. 2014. Unsupervised inference of significant locations from wifi data for understanding human dynamics. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia. 232--235.
    [28]
    A. Alvarez-Alvarez, J. M. Alonso, and G. Trivino. 2013. Human activity recognition in indoor environments by means of fusing information extracted from intensity of WiFi signal and accelerations. Inf. Sci. (N. Y.). 233 (2013), 162--182. https://doi.org/10.1016/j.ins.2013.01.029.
    [29]
    H. Hong, C. Luo, and M. C. Chan. 2016. Socialprobe: Understanding social interaction through passive wifi monitoring. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. 94--103.
    [30]
    Z. Chen, Y. Chen, S. Wang, J. Liu, X. Gao, and A. T. Campbell. 2013. Inferring social contextual behavior from bluetooth traces. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication 2013, 267--270.
    [31]
    T.-F. Wu, C.-J. Lin, and R. C. Weng. 2004. Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5 (August 2004), 975--1005. https://www.jmlr.org/papers/v5/wu04a.html?907d3908.
    [32]
    J. Wu, L. Sun, and R. Jafari. 2016. A wearable system for recognizing american sign language in real-time using IMU and surface EMG sensors. IEEE J. Biomed. Heal. Inf. 20, 5 (2016), 1281--1290.
    [33]
    J. Wu and R. Jafari. 2018. Orientation independent activity/gesture recognition using wearable motion sensors. IEEE IoT J. 6, 2 (2018), 1427--1437.

    Cited By

    View all
    • (2023)Noninvasive Multimodal Physiological Sensing SystemsEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00094-7(236-252)Online publication date: 2023
    • (2021)Monitoring Activities of Daily Living with a Mobile App and Bluetooth Beacons2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9659964(1-8)Online publication date: 5-Dec-2021

    Index Terms

    1. Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Transactions on Computing for Healthcare
      ACM Transactions on Computing for Healthcare  Volume 2, Issue 2
      April 2021
      226 pages
      EISSN:2637-8051
      DOI:10.1145/3446675
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 04 January 2021
      Accepted: 01 September 2020
      Revised: 01 August 2020
      Received: 01 February 2020
      Published in HEALTH Volume 2, Issue 2

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Bluetooth Low Energy
      2. Context detection
      3. activity recognition
      4. context-aware
      5. nearables
      6. wearables

      Qualifiers

      • Research-article
      • Research
      • Refereed

      Funding Sources

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)27
      • Downloads (Last 6 weeks)0

      Other Metrics

      Citations

      Cited By

      View all
      • (2023)Noninvasive Multimodal Physiological Sensing SystemsEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00094-7(236-252)Online publication date: 2023
      • (2021)Monitoring Activities of Daily Living with a Mobile App and Bluetooth Beacons2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9659964(1-8)Online publication date: 5-Dec-2021

      View Options

      Get Access

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media