research-article

Public Access

MiShape: Accurate Human Silhouettes and Body Joints from Commodity Millimeter-Wave Devices

Authors:

Aakriti Adhikari,

Sanjib Sur, and

Srihari NelakuditiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 6, Issue 3

Article No.: 96, Pages 1 - 31

https://doi.org/10.1145/3550300

Published: 07 September 2022 Publication History

Abstract

We propose MiShape, a millimeter-wave (mmWave) wireless signal based imaging system that generates high-resolution human silhouettes and predicts 3D locations of body joints. The system can capture human motions in real-time under low light and low-visibility conditions. Unlike existing vision-based motion capture systems, MiShape is privacy non-invasive and can generalize to a wide range of motion tracking applications at-home. To overcome the challenges with low-resolution, specularity, and aliasing in images from Commercial-Off-The-Shelf (COTS) mmWave systems, MiShape designs deep learning models based on conditional Generative Adversarial Networks and incorporates the rules of human biomechanics. We have customized MiShape for gait monitoring, but the model is well adaptive to any tracking applications with limited fine-tuning samples. We experimentally evaluate MiShape with real data collected from a COTS mmWave system for 10 volunteers, with diverse ages, gender, height, and somatotype, performing different poses. Our experimental results demonstrate that MiShape delivers high-resolution silhouettes and accurate body poses on par with an existing vision-based system, and unlocks the potential of mmWave systems, such as 5G home wireless routers, for privacy-noninvasive healthcare applications.

References

[1]

Yao, Lina and Sheng, Quan Z. and Ruan, Wenjie and Gu, Tao and Li, Xue and Falkner, Nick and Yang, Zhi, "RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array," in Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services on 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2015.

[2]

Wang, Xueyi and Ellul, Joshua and Azzopardi, George, "Elderly Fall Detection Systems: A Literature Survey," Frontiers in Robotics and AI, vol. 7, 2020.

[3]

Dawes, A J and Lin, A Y and Varghese, C and Russell, M M and Lin, A Y, "Mobile Health Technology for Remote Home Monitoring After Surgery: A Meta-Analysis," British Journal of Surgery, vol. 21, 10 2021.

[4]

Ghazi, Mustafa A. and Ding, Lei and Fagg, Andrew H. and Kolobe, Thubi H.A. and Miller, David P., "Vision-Based Motion Capture System for Tracking Crawling Motions of Infants," in 2017 IEEE International Conference on Mechatronics and Automation (ICMA), 2017.

[5]

Anh L. Bui and Gregg C. Fonarow, "Home Monitoring for Heart Failure Management," Journal of the American College of Cardiology, vol. 59, no. 2, 2012.

[6]

Sica, Marco et al, "Continuous Home Monitoring of Parkinson's Disease using Inertial Sensors," PLOS ONE, no. 2, 2021.

[7]

Tiersen, Federico et al, "Smart Home Sensing and Monitoring in Households With Dementia," JMIR Aging, no. 3, 2021.

[8]

Buzzelli, Marco and Albe, Alessio and Ciocca, Gianluigi, "A Vision-Based System for Monitoring Elderly People at Home," Applied Sciences, vol. 10, no. 1, 2020.

[9]

Gutierrez J, Rodriguez V and Martin S, "Comprehensive Review of Vision-Based Fall Detection Systems," Sensors, vol. 21, no. 3, 2021.

[10]

Y. Feng and L. Max, "Accuracy and Precision of a Custom Camera-Based System for 2-D and 3-D Motion Tracking During Speech and Nonspeech Motor Tasks," Journal of Speech, Language, and Hearing Research, vol. 57, 2014.

[11]

Sun Guanghao, et al., Noncontact Monitoring of Vital Signs with RGB and Infrared Camera and Its Application to Screening of Potential Infection, 1st ed. IntechOpen, 2018.

[12]

Michael Furst and Shriya T. P. Gupta and Rene Schuster and Oliver Wasenmuller and Didier Stricker, "HPERL: 3D Human Pose Estimation from RGB and LiDAR," 2020. [Online]. Available: https://arxiv.org/abs/2010.08221

[13]

Guan, Junfeng and Madani, Sohrab and Jog, Suraj and Gupta, Saurabh and Hassanieh, Haitham, "Through Fog High-Resolution Imaging Using Millimeter Wave Radar," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

[14]

Verizon, "Verizon 5G Home Router," 2022. [Online]. Available: https://www.verizon.com/support/knowledge-base-220089/

[15]

Roger Appleby, Duncan A. Robertson and David Wikner, "Millimeter Wave Imaging: A Historical Review," in Proc. SPIE, 2017.

[16]

"ProVision Automatic Target Detection," 2015. [Online]. Available: http://www.sds.l-3com.com/advancedimaging/provision-at.htm

[17]

K. Mowery, E. Wustrow, T. Wypych, C. Singleton, C. Comfort, E. Rescorla, S. C. J. A. Halderman, and H. Shacham, "Security Analysis of a Full-Body Scanner," in USENIX Security Symposium, 2014.

[18]

Jiang, Wenjun and Xue, Hongfei and Miao, Chenglin and Wang, Shiyang and Lin, Sen and Tian, Chong and Murali, Srinivasan and Hu, Haochen and Sun, Zhi and Su, Lu, "Towards 3D Human Pose Construction Using WiFi," in Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, 2020.

[19]

Zhao, Mingmin and Li, Tianhong and Alsheikh, Mohammad Abu and Tian, Yonglong and Zhao, Hang and Torralba, Antonio and Katabi, Dina, "Through-Wall Human Pose Estimation Using Radio Signals," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.

[20]

Fadel Adib and Zach Kabelac and Dina Katabi and Robert C. Miller, "3D Tracking via Body Radio Reflections," in 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14), 2014.

[21]

F. Adib, C.-Y. Hsu, H. Mao, D. Katabi, and F. Durand, "Capturing the Human Figure Through a Wall," in ACM SIGGRAPH Asia, 2015.

[22]

C. S. Pros, "CCTV." [Online]. Available: https://www.cctvsecuritypros.com

[23]

"RoboRealm. Microsoft Kinect, 2013." [Online]. Available: http://www.roborealm.com/help/MicrosoftKinect.php

[24]

"Vicon." [Online]. Available: https://www.vicon.com/applications/life-sciences/gait-analysis-neuroscience-and-motor-control/

[25]

Zhang, Feng and Wu, Chenshu and Wang, Beibei and Liu, K. J. Ray, "mmEye: Super-Resolution Millimeter Wave Imaging," IEEE Internet of Things Journal, vol. 8, no. 8, 2021.

[26]

Xue, Hongfei and Ju, Yan and Miao, Chenglin and Wang, Yijiang and Wang, Shiyang and Zhang, Aidong and Su, Lu, "mmMesh: Towards 3D Real-Time Dynamic Human Mesh Construction Using Millimeter-Wave," in Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, 2021.

[27]

M. Skolnik, Introduction to Radar Systems. McGraw-Hill Book Co., 1962.

[28]

IEEE Standards Association, "IEEE Standards 802.11ad-2012, Amendment 3: Enhancements for Very High Throughput in the 60 GHz Band," goo.gl/r2JeYd, 2012.

[29]

Wu, Ting and Rappaport, Theodore S. and Collins, Christopher M., "The Human Body and Millimeter-Wave Wireless Communication Systems: Interactions and Implications," in 2015 IEEE International Conference on Communications (ICC), 2015.

[30]

Sanjib Sur and Vignesh Venkateswaran and Xinyu Zhang and Parmesh Ramanathan, "60 GHz Indoor Networking through Flexible Beams: A Link-Level Profiling," in Proc. of ACM SIGMETRICS, 2015.

[31]

Adib, Fadel and Katabi, Dina, "See through Walls with WiFi!" in Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, 2013.

[32]

Zhao, Mingmin and Liu, Yingcheng and Raghu, Aniruddh and Zhao, Hang and Li, Tianhong and Torralba, Antonio and Katabi, Dina, "Through-Wall Human Mesh Recovery Using Radio Signals," in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019.

[33]

Wang, Weiwei and Yang, Kehu, "A Method for Millimeter-Wave Imaging of Concealed Objects Via De-Aliasing," in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.

[34]

Kazemi, Mahmoud and Kavehvash, Zahra and Shabany, Mahdi, "K-Space Analysis of Aliasing in Millimeter-Wave Imaging Systems," IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 3, 2021.

[35]

Gao, Xiangyu and Roy, Sumit and Xing, Guanbin, "MIMO-SAR: A Hierarchical High-Resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving," IEEE Transactions on Vehicular Technology, vol. 70, no. 8, 2021.

[36]

Jing, Handan and Li, Shiyong and Miao, Ke and Wang, Shuoguang and Cui, Xiaoxi and Zhao, Guoqiang and Sun, Houjun, "Enhanced Millimeter-Wave 3-D Imaging via Complex-Valued Fully Convolutional Neural Network," Electronics, vol. 11, no. 1, 2022.

[37]

Lam H. Nguyen, "Millimeter-wave forward-looking 3-D SAR imaging challenges," in Passive and Active Millimeter-Wave Imaging, 2019.

[38]

Evan C. Zaugg and David G. Long, "Generalized Frequency Scaling and Backprojection for LFM-CW SAR Processing," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, 2015.

[39]

Watts, Claire M. and Lancaster, Patrick and Pedross-Engel, Andreas and Smith, Joshua R. and Reynolds, Matthew S., "2D and 3D Millimeter-Wave Synthetic Aperture Radar Imaging on a PR2 Platform," in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.

[40]

Texas Instruments, "IWR1443 Single-Chip 76-GHz to 81-GHz MmWave Sensor Evaluation Module," 2020. [Online]. Available: https://www.ti.com/tool/IWR1443BOOST

[41]

Gamesto, "Kinect," 2022. [Online]. Available: https://www.gamestop.com/gaming-accessories/controllers/xbox-one/products/microsoft-kinect-for-xbox-one/10115985.html

[42]

Hao Zhu and Xinxin Zuo and Sen Wang and Xun Cao and Ruigang Yang, "Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation," 2019. [Online]. Available: https://arxiv.org/abs/1904.10506

[43]

Wang, Z. and Simoncelli, E.P. and Bovik, A.C., "Multiscale Structural Similarity for Image Quality Assessment," in The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, 2003.

[44]

Mehrdad Soumekh, Synthetic Aperture Radar Signal Processing, 1st ed. John Wiley & Sons, Inc., 1999.

[45]

NETGEAR, Inc., "Nighthawk X10 Smart WiFi Router," 2022. [Online]. Available: https://www.netgear.com/landings/ad7200/

[46]

TP-Link Corporation Limited, "Talon AD7200 Multi-Band Wi-Fi Router," 2022. [Online]. Available: https://www.tp-link.com/us/home-networking/wifi-router/ad7200/

[47]

IgniteNet, "MetroLinq 10G Tri-Band Omni," 2022. [Online]. Available: https://www.ignitenet.com/wireless-backhaul/ml-10g-omni/

[48]

MikroTik, "wAP 60G," 2022. [Online]. Available: https://mikrotik.com/product/wap_60g

[49]

Sivers Semiconductors AB, "60 GHz Evaluation Kits (EVK)," 2022. [Online]. Available: https://www.sivers-semiconductors.com/sivers-wireless/evaluation-kits/

[50]

Rojhani, Neda and Passafiume, Marco and Lucarelli, Matteo and Collodi, Giovanni and Cidronali, Alessandro, "Assessment of Compressive Sensing 2x2 MIMO Antenna Design for Millimeter-Wave Radar Image Enhancement," Electronics, vol. 9, no. 4, 2020.

[51]

Airfide Networks, "Airfide Brings High Performance Home and Enterprise 5G-NR Wireless," 2022. [Online]. Available: https://airfidenet.com/

[52]

Xinyu Zhang, "M-Cube: An Open-Source Programmable Millimeter-Wave Experimental Platform," 2022. [Online]. Available: http://m3.ucsd.edu/

[53]

tsne, "tsne," 2021. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html

[54]

Brekel, "Face V1," 2022. [Online]. Available: https://brekel.com/

[55]

Cleveland, Jerika and Lewis, Jacob and Mitra, Dipankar and Braaten, Benjamin D and Allen, Jeffery and Allen, Monica, "On the Image Analysis of Conducting Magneto-Responsive Micro-Particles for Applications in Leaky Wave Antenna Beam Steering," in 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting. IEEE, 2020.

[56]

Lisa Jamhoury, "Understanding Kinect V2 Joints and Coordinate System," 2022. [Online]. Available: https://lisajamhoury.medium.com/understanding-kinect-v2-joints-and-coordinate-system-4f4b90b9df16

[57]

Schellberg, Jacqueline M and Sur, Sanjib, ViSAR: A Mobile Platform for Vision-Integrated Millimeter-Wave Synthetic Aperture Radar. Association for Computing Machinery, 2021.

[58]

Xun Huang and Yixuan Li and Omid Poursaeed and John Hopcroft and Serge Belongie, "Stacked Generative Adversarial Networks," 2017. [Online]. Available: https://arxiv.org/abs/1612.04357

[59]

Denton, Emily L and Chintala, Soumith and szlam, arthur and Fergus, Rob, "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks," in Advances in Neural Information Processing Systems, 2015.

[60]

Xiaolong Wang and Abhinav Gupta, "Generative Image Modeling using Style and Structure Adversarial Networks," 2016. [Online]. Available: https://arxiv.org/abs/1603.05631

[61]

Goodfellow, Ian, et al., "Generative Adversarial Networks," Commun. ACM, vol. 63, no. 11, 2020.

[62]

Mehdi Mirza and Simon Osindero, "Conditional Generative Adversarial Nets," 2014. [Online]. Available: https://arxiv.org/abs/1411.1784

[63]

R. E. Carlson and F. N. Fritsch, "Monotone Piecewise Bicubic Interpolation," SIAM Journal on Numerical Analysis, vol. 22, no. 2, 1985.

Digital Library

[64]

Song, Yibing and Gong, Lijun, "Analysis and Improvement of Joint Bilateral Upsampling for Depth Image Super-Resolution," in 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP). IEEE, 2016.

[65]

Christian Ledig and Lucas Theis and Ferenc Huszar and Jose Caballero and Andrew Cunningham and Alejandro Acosta and Andrew Aitken and Alykhan Tejani and Johannes Totz and Zehan Wang and Wenzhe Shi, "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network," 2017. [Online]. Available: https://arxiv.org/abs/1609.04802

[66]

Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li and Li Fei-Fei, "ImageNet: A Large-Scale Hierarchical Image Database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.

[67]

Karen Simonyan and Andrew Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," 2015. [Online]. Available: https://arxiv.org/abs/1409.1556

[68]

Roegiers, Sanne et al., "Human Action Recognition using Hierarchic Body Related Occupancy Maps," Integrated Computer-Aided Engineering, vol. 26, no. 3, 2019.

[69]

G. Chen, C. Patten, D. H. Kothari, and F. E. Zajac, "Gait Deviations Associated with Post-Stroke Hemiparesis: Improvement During Treadmill Walking using Weight Support, Speed, Support Stiffness, and Handrail Hold," Gait & Posture, vol. 22, no. 1, 2005.

[70]

Anouk Lamontagne and Joyce Fung, "Faster is Better: Implications for Speed-Intensive Gait Training After Stroke," Stroke, vol. 35, no. 11, 2004.

[71]

Neil F. Gordon and Meg Gulanick and Fernando Costa and Gerald Fletcher and Barry A. Franklin and Elliot J. Roth and Tim Shephard, "Physical Activity and Exercise Recommendations for Stroke Survivors: An American Heart Association Scientific Statement from the Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention; the Council on Cardiovascular Nursing; the Council on Nutrition, Physical Activity, and Metabolism; and the Stroke Council," Circulation, vol. 109, no. 16, 2004.

[72]

Stacy L. Fritz and Ashlee L. Pittman and Anna C. Robinson and Skylar C. Orton and Erin D. Rivers, "An Intense Intervention for Improving Gait, Balance, and Mobility for Individuals with Chronic Stroke: A Pilot Study," Journal of Neurologic Physical Therapy, vol. 31, no. 2, 2007.

[73]

Chen-Yu Hsu, et al., "Enabling Identification and Behavioral Sensing in Homes using Radio Reflections," in ACM CHI, 2019.

[74]

Wang, Wei and Liu, Alex X. and Shahzad, Muhammad, "Gait Recognition Using Wifi Signals," in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016.

[75]

Gerald F. Harris and Jacqueline J. Wertsch, "Procedures for gait analysis," Archives of Physical Medicine and Rehabilitation, vol. 75, no. 2, 1994.

[76]

John H. Hollman and Eric M. McDade and Ronald C. Petersen, "Normative Spatiotemporal Gait Parameters in Older Adults," Gait and Posture, vol. 34, no. 1, 2011.

[77]

Amanda E. Chisholm and Shelley Makepeace and Elizabeth L. Inness and Stephen D. Perry and William E. McIlroy and Avril Mansfield, "Spatial-Temporal Gait Variability Poststroke: Variations in Measurement and Implications for Measuring Change," Archives of Physical Medicine and Rehabilitation, vol. 95, no. 7, 2014.

[78]

Ion Martinikorena and Alicia MartÃ&eng;nez-Ramirez and Marisol Gomez and Pablo Lecumberri and Alvaro Casas-Herrero and Eduardo L. Cadore and Nora Millor and Fabricio Zambom-Ferraresi and Fernando Idoate and Mikel Izquierdo, "Gait Variability Related to Muscle Quality and Muscle Power Output in Frail Nonagenarian Older Adults," Journal of the American Medical Directors Association, vol. 17, no. 2, 2016.

[79]

Renata Noce Kirkwood and Bruno de Souza Moreira and Marcia L.D.C. Vallone and Sueli Aparecida Mingoti and Rosangela Correa Dias and Rosana Ferreira Sampaio, "Step Length Appears to be a Strong Discriminant Gait Parameter for Elderly Females Highly Concerned about Falls: A Cross-Sectional Observational Study," Physiotherapy, vol. 97, no. 2, 2011.

[80]

Texas Instruments, "DCA1000EVM: Real-time Data-Capture Adapter for Radar Sensing Evaluation Module," 2020. [Online]. Available: https://www.ti.com/tool/DCA1000EVM

[81]

Palermo Physiotherapy, "Palermo Physiotherapy and Wellness Center," 2022. [Online]. Available: https://palermophysio.ca/yoga-basics-part-2-most-common-poses/

[82]

Henry, Kristin D and Rosemond, Cherie and Eckert, Lynn B, "Effect of number of home exercises on compliance and performance in adults over 65 years of age," Physical Therapy, vol. 79, no. 3, 1999.

[83]

Moraes, Alberto da Rocha and Sanches, Monique Lalue and Ribeiro, Eduardo Cotecchia and Guimarães, Antonio Sérgio, "Therapeutic exercises for the control of temporomandibular disorders," Dental press journal of orthodontics, vol. 18, no. 5, 2013.

[84]

Ann Pizer, "VeryWellFit," 2022. [Online]. Available: https://www.verywellfit.com/essential-yoga-poses-for-beginners-3566747

[85]

Open-Source, "TensorFlow," 2022. [Online]. Available: https://www.tensorflow.org/

[86]

Open-Source, "Spyder IDE," 2022. [Online]. Available: https://www.spyder-ide.org/

[87]

Open-Source, "ANACONDA," 2022. [Online]. Available: https://www.anaconda.com/

[88]

NVIDIA, "GEFORCE," 2022. [Online]. Available: https://www.nvidia.com/en-us/geforce/

[89]

Google, "Cloud TPU," 2022. [Online]. Available: https://cloud.google.com/tpu

[90]

Ferzli, Rony and Karam, Lina J, "A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)," IEEE transactions on image processing, vol. 18, no. 4, 2009.

[91]

Tolga Bridal, "Sharpness Estimation From Image Gradients," 2022. [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/32397-sharpness-estimation-from-image-gradients

[92]

Mortazavi, Fatemeh and Nadian, Ali, "Stability of Kinect for Range of Motion Analysis in Static Stretching Exercises," PLOS ONE, vol. 13, p. e0200992, 07 2018.

[93]

Zhu, Weiqiang and Mousavi, S Mostafa and Beroza, Gregory C, "Seismic signal augmentation to improve generalization of deep neural networks," in Advances in geophysics. Elsevier, 2020, vol. 61, pp. 151--177.

[94]

Regmi, Hem and Saadat, Moh Sabbir and Sur, Sanjib and Nelakuditi, Srihari, "SquiggleMilli: Approximating SAR Imaging on Mobile Millimeter-Wave Devices," Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 5, no. 3, 2021.

[95]

IEEE Standards Association, "IEEE Standards 802.11ad-2012: Enhancements for Very High Throughput in the 60 GHz Band," 2012.

[96]

Texas Instruments, "IWR6843 Single-Chip 60-GHz MmWave Sensor Evaluation Module," 2020. [Online]. Available: https://www.ti.com/tool/IWR6843ISK

[97]

George Papandreou and Tyler Zhu and Nori Kanazawa and Alexander Toshev and Jonathan Tompson and Chris Bregler and Kevin Murphy, "Towards Accurate Multi-person Pose Estimation in the Wild," 2017. [Online]. Available: https://arxiv.org/abs/1701.01779

[98]

Zhe Cao and Gines Hidalgo and Tomas Simon and Shih-En Wei and Yaser Sheikh, "OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields," 2019. [Online]. Available: https://arxiv.org/abs/1812.08008

[99]

Khalife, Joe and Ragothaman, Sonya and Kassas, Zaher M., "Pose Estimation with LiDAR Odometry and Cellular Pseudoranges," in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017.

[100]

Tasnim, Nusrat and Islam, Mohammad Khairul and Baek, Joong-Hwan, "Deep Learning Based Human Activity Recognition Using Spatio-Temporal Image Formation of Skeleton Joints," Applied Sciences, vol. 11, no. 6, 2021.

[101]

Zhao, Mingmin and Tian, Yonglong and Zhao, Hang and Alsheikh, Mohammad Abu and Li, Tianhong and Hristov, Rumen and Kabelac, Zachary and Katabi, Dina and Torralba, Antonio, "RF-Based 3D Skeletons," in Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, 2018.

Digital Library

[102]

Jin, Haojian and Yang, Zhijian and Kumar, Swarun and Hong, Jason I., "Towards Wearable Everyday Body-Frame Tracking Using Passive RFIDs," Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 1, no. 4, 2018.

[103]

Anderson, Boyd and Shi, Mingqian and Tan, Vincent Y. F. and Wang, Ye, "Mobile Gait Analysis Using Foot-Mounted UWB Sensors," Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 3, no. 3, 2019.

[104]

Sengupta, Arindam and Jin, Feng and Zhang, Renyuan and Cao, Siyang, "mm-Pose: Real-Time Human Skeletal Posture Estimation Using mmWave Radars and CNNs," IEEE Sensors Journal, vol. 20, no. 17, 2020.

[105]

Arindam Sengupta and Siyang Cao, "mmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars," 2021. [Online]. Available: https://arxiv.org/abs/2107.10327

[106]

Qian, Kun and He, Zhaoyuan and Zhang, Xinyu, "3D Point Cloud Generation with Millimeter-Wave Radar," Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 4, no. 4, 2020.

[107]

Egiazarian, Karen and Katkovnik, Vladimir, "Single Image Super-Resolution via BM3D Sparse Coding," in 2015 23rd European Signal Processing Conference (EUSIPCO), 2015.

[108]

Chao Dong and Chen Change Loy and Kaiming He and Xiaoou Tang, "Image Super-Resolution Using Deep Convolutional Networks," 2015. [Online]. Available: https://arxiv.org/abs/1501.00092

[109]

W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, and Q. Liao, "Deep Learning for Single Image Super-Resolution: A Brief Review," IEEE Transactions on Multimedia, vol. 21, no. 12, 2019.

Digital Library

[110]

Sabbir Saadat and Sanjib Sur and Srihari Nelakuditi and Parmesh Ramanathan, "MilliCam: Hand-held Millimeter-Wave Imaging," in IEEE International Conference on Computer Communications and Networks (ICCCN), 2020.

Cited By

Regmi HSur S(2024)CoSense: Deep Learning Augmented Sensing for Coexistence with Networking in Millimeter-Wave PicocellsACM Transactions on Internet of Things10.1145/3670415Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1145/3670415
Fan LXie LZhou WWang CBu YLu S(2024)Beamforming for Sensing: Hybrid Beamforming based on Transmitter-Receiver Collaboration for Millimeter-Wave SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596198:2(1-27)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659619
Xu ABiehl JLee A(2024)Getting it Just Right: Towards Balanced Utility, Privacy, and Equity in Shared Space SensingACM Transactions on Internet of Things10.1145/36484795:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3648479
Show More Cited By

Index Terms

MiShape: Accurate Human Silhouettes and Body Joints from Commodity Millimeter-Wave Devices
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

Recommendations

Efficient Frequency Reconfigurable Antenna for 4G, Sub-6 GHz, 5G Portable Devices Applications
Abstract
This research investigates a frequency-switchable, multi-mode operation antenna contingent on ground structure slots and suitable for 4G, 5G/sub 6 GHz and other modern systems. In earlier research work, researchers used the approach of putting ...
Read More
High gain substrate integrated waveguide antenna with enhanced bandwidth for millimeter-wave wireless network applications
Abstract
A high-gain slotted waveguide antenna in substrate-integrated waveguide (SIW) configuration with enhanced bandwidth is proposed. Two different cases fabricated using Rogers RO 4003 substrate over a small area of 16 × 4.5 × 0.508 mm³ are discussed. ...
Read More
Design and Analysis of a High Gain Millimeter-Wave Antenna Array for Dual Purpose Applications
Abstract
Since higher band of 5G handles the frequencies from 24 to 47 GHz near the basement of the millimeter-wave band and also Ka-band which uses the frequency from 27 to 40 GHz for satellite communication, in this work an antenna array is designed at ...
Read More

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 6, Issue 3

September 2022

1612 pages

EISSN:2474-9567

DOI:10.1145/3563014

Issue’s Table of Contents

Copyright © 2022 ACM.

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: 07 September 2022

Published in IMWUT Volume 6, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

NSF (National Science Foundation)
UofSC

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
1,114
Total Downloads

Downloads (Last 12 months)473
Downloads (Last 6 weeks)78

Other Metrics

View Author Metrics

Citations

Cited By

Regmi HSur S(2024)CoSense: Deep Learning Augmented Sensing for Coexistence with Networking in Millimeter-Wave PicocellsACM Transactions on Internet of Things10.1145/3670415Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1145/3670415
Fan LXie LZhou WWang CBu YLu S(2024)Beamforming for Sensing: Hybrid Beamforming based on Transmitter-Receiver Collaboration for Millimeter-Wave SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596198:2(1-27)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659619
Xu ABiehl JLee A(2024)Getting it Just Right: Towards Balanced Utility, Privacy, and Equity in Shared Space SensingACM Transactions on Internet of Things10.1145/36484795:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3648479
Fu YZhang YLu YQiu LChen YWang YWang MLi YRen JZhang YOkoshi TKo JLiKamWa R(2024)Adaptive Metasurface-Based Acoustic Imaging using Joint OptimizationProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661863(492-504)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661863
Liu CDong ZHuang LYan WWang XFang DChen X(2024)TagSleep3DProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435128:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643512
Zhang DZhang XXie YZhang FWang XLi YZhang D(2024)LoCalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314367:4(1-27)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631436
Richer RKoch VAbel LHauck FKurz MRinggold VMüller VKüderle ASchindler-Gmelch LEskofier BRohleder N(2024)Machine learning-based detection of acute psychosocial stress from body posture and movementsScientific Reports10.1038/s41598-024-59043-114:1Online publication date: 8-Apr-2024
https://doi.org/10.1038/s41598-024-59043-1
Amaya KIsogawa M(2023)Adaptive and Robust Mmwave-Based 3D Human Mesh Estimation for Diverse Poses2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222059(455-459)Online publication date: 8-Oct-2023
https://doi.org/10.1109/ICIP49359.2023.10222059
Sitar ESur S(2022)MilliFit: Millimeter-Wave Wireless Sensing Based At-Home Exercise Classification2022 18th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN57253.2022.00036(150-154)Online publication date: Dec-2022
https://doi.org/10.1109/MSN57253.2022.00036

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents