{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:45Z","timestamp":1787029545552,"version":"3.56.0"},"reference-count":182,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union project HUMANE-AI-NET","award":["H2020-ICT-2019-3 #952026"],"award-info":[{"award-number":["H2020-ICT-2019-3 #952026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid\/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.<\/jats:p>","DOI":"10.3390\/s22124596","type":"journal-article","created":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T21:19:26Z","timestamp":1655673566000},"page":"4596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6760-5539","authenticated-orcid":false,"given":"Sizhen","family":"Bian","sequence":"first","affiliation":[{"name":"German Research Centre for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0527-1208","authenticated-orcid":false,"given":"Mengxi","family":"Liu","sequence":"additional","affiliation":[{"name":"German Research Centre for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8976-5960","authenticated-orcid":false,"given":"Bo","family":"Zhou","sequence":"additional","affiliation":[{"name":"German Research Centre for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Lukowicz","sequence":"additional","affiliation":[{"name":"German Research Centre for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sigg, S., Shi, S., Buesching, F., Ji, Y., and Wolf, L. (2013, January 2\u20134). Leveraging RF-channel fluctuation for activity recognition: Active and passive systems, continuous and RSSI-based signal features. Proceedings of the International Conference on Advances in Mobile Computing & Multimedia, Vienna, Austria.","DOI":"10.1145\/2536853.2536873"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ramos, R.G., Domingo, J.D., Zalama, E., and G\u00f3mez-Garc\u00eda-Bermejo, J. (2021). Daily Human Activity Recognition Using Non-Intrusive Sensors. Sensors, 21.","DOI":"10.3390\/s21165270"},{"key":"ref_3","unstructured":"Samadi, M.R.H., and Cooke, N. (2014, January 1\u20135). EEG signal processing for eye tracking. Proceedings of the 2014 22nd European Signal Processing Conference (EUSIPCO), Lisbon, Portugal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102738","DOI":"10.1016\/j.jnca.2020.102738","article-title":"A review and categorization of techniques on device-free human activity recognition","volume":"167","author":"Hussain","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1108\/SR-11-2017-0245","article-title":"An overview of human activity recognition based on smartphone","volume":"39","author":"Yuan","year":"2019","journal-title":"Sens. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.1109\/COMST.2019.2897610","article-title":"A survey of underwater magnetic induction communications: Fundamental issues, recent advances, and challenges","volume":"21","author":"Li","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107561","DOI":"10.1016\/j.patcog.2020.107561","article-title":"Sensor-based and vision-based human activity recognition: A comprehensive survey","volume":"108","author":"Dang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83791","DOI":"10.1109\/ACCESS.2020.2991891","article-title":"Sensing technology for human activity recognition: A comprehensive survey","volume":"8","author":"Fu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","first-page":"271","article-title":"Survey and analysis of human activity recognition in surveillance videos","volume":"13","author":"Raval","year":"2019","journal-title":"Intell. Decis. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.14257\/ijsh.2017.11.6.03","article-title":"Multi resident complex activity recognition in smart home: A literature review","volume":"11","author":"Mohamed","year":"2017","journal-title":"Int. J. Smart Home"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bux, A., Angelov, P., and Habib, Z. (2017). Vision based human activity recognition: A review. Adv. Comput. Intell. Syst., 341\u2013371.","DOI":"10.1007\/978-3-319-46562-3_23"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ma, J., Wang, H., Zhang, D., Wang, Y., and Wang, Y. (2016, January 18\u201321). A survey on wi-fi based contactless activity recognition. Proceedings of the International Conference on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart World Congress (UIC\/ATC\/ScalCom\/CBDCom\/IoP\/SmartWorld), Toulouse, France.","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0170"},{"key":"ref_13","unstructured":"Lioulemes, A., Papakostas, M., Gieser, S.N., Toutountzi, T., Abujelala, M., Gupta, S., Collander, C., Mcmurrough, C.D., and Makedon, F. (July, January 29). A survey of sensing modalities for human activity, behavior, and physiological monitoring. Proceedings of the PETRA \u201916: 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Corfu Island, Greece."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/frobt.2015.00028","article-title":"A review of human activity recognition methods","volume":"2","author":"Vrigkas","year":"2015","journal-title":"Front. Robot. AI"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/JSEN.2014.2370945","article-title":"Wearable sensors for human activity monitoring: A review","volume":"15","author":"Mukhopadhyay","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1177\/1420326X12469714","article-title":"Human activity recognition via recognized body parts of human depth silhouettes for residents monitoring services at smart home","volume":"22","author":"Jalal","year":"2013","journal-title":"Indoor Built Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102970","DOI":"10.1016\/j.scs.2021.102970","article-title":"A smartphone sensors-based personalized human activity recognition system for sustainable smart cities","volume":"71","author":"Javed","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_18","unstructured":"Li, R., Chellappa, R., and Zhou, S.K. (2009, January 20\u201325). Learning multi-modal densities on discriminative temporal interaction manifold for group activity recognition. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1555007","DOI":"10.1142\/S0218001415550071","article-title":"Group activity recognition with group interaction zone based on relative distance between human objects","volume":"29","author":"Cho","year":"2015","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2630413","DOI":"10.1155\/2017\/2630413","article-title":"Evolution of indoor positioning technologies: A survey","volume":"2017","author":"Brena","year":"2017","journal-title":"J. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmcj.2010.07.001","article-title":"Indoor tracking for mission critical scenarios: A survey","volume":"7","author":"Fuchs","year":"2011","journal-title":"Pervasive Mob. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1109\/TRO.2021.3111786","article-title":"Proximity perception in human-centered robotics: A survey on sensing systems and applications","volume":"38","author":"Navarro","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.neucom.2011.09.037","article-title":"A survey on fall detection: Principles and approaches","volume":"100","author":"Mubashir","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1630003","DOI":"10.1142\/S0219519416300039","article-title":"Gait analysis: Systems, technologies, and importance","volume":"16","author":"Akhtaruzzaman","year":"2016","journal-title":"J. Mech. Med. Biol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.ejpain.2008.04.006","article-title":"Assessment of physical activity in daily life in patients with musculoskeletal pain","volume":"13","author":"Verbunt","year":"2009","journal-title":"Eur. J. Pain"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"35553","DOI":"10.1007\/s11042-019-08328-z","article-title":"A review of emotion sensing: Categorization models and algorithms","volume":"79","author":"Wang","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Arakawa, T. (2021). A Review of Heartbeat Detection Systems for Automotive Applications. Sensors, 21.","DOI":"10.3390\/s21186112"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1016\/j.comnet.2010.05.003","article-title":"Wireless sensor networks for healthcare: A survey","volume":"54","author":"Alemdar","year":"2010","journal-title":"Comput. Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"68022","DOI":"10.1109\/ACCESS.2019.2917125","article-title":"SmartWall: Novel RFID-enabled ambient human activity recognition using machine learning for unobtrusive health monitoring","volume":"7","author":"Oguntala","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/THMS.2014.2377111","article-title":"Human activity recognition process using 3-D posture data","volume":"45","author":"Gaglio","year":"2014","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111738","DOI":"10.1016\/j.sna.2019.111738","article-title":"A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition","volume":"301","author":"Jiang","year":"2020","journal-title":"Sens. Actuators A Phys."},{"key":"ref_32","unstructured":"Elniema Abdrahman Abdalla, H. (2021). Hand Gesture Recognition Based on Time-of-Flight Sensors. [Ph.D Thesis, Politecnico di Torino]."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nahler, C., Plank, H., Steger, C., and Druml, N. (December, January 29). Resource-Constrained Human Presence Detection for Indirect Time-of-Flight Sensors. Proceedings of the 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia.","DOI":"10.1109\/DICTA52665.2021.9647286"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hossen, M.A., Zahir, E., Ata-E-Rabbi, H., Azam, M.A., and Rahman, M.H. (2021, January 10\u201313). Developing a Mobile Automated Medical Assistant for Hospitals in Bangladesh. Proceedings of the 2021 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA.","DOI":"10.1109\/AIIoT52608.2021.9454236"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7000708","DOI":"10.1109\/TIM.2022.3141159","article-title":"Multitouch Pressure Sensing with Soft Optical Time-of-Flight Sensors","volume":"71","author":"Lin","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bortolan, G., Christov, I., and Simova, I. (2021). Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091678"},{"key":"ref_37","unstructured":"Ismail, M.I.M., Dzyauddin, R.A., Samsul, S., Azmi, N.A., Yamada, Y., Yakub, M.F.M., and Salleh, N.A.B.A. (2019). An RSSI-based Wireless Sensor Node Localisation using Trilateration and Multilateration Methods for Outdoor Environment. arXiv."},{"key":"ref_38","first-page":"1","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"Chen","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1006\/cviu.1998.0744","article-title":"Human motion analysis: A review","volume":"73","author":"Aggarwal","year":"1999","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_40","unstructured":"Kellokumpu, V., Pietik\u00e4inen, M., and Heikkil\u00e4, J. (2005, January 16\u201318). Human activity recognition using sequences of postures. Proceedings of the MVA, Tsukuba, Japan."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"M\u00fcnzner, S., Schmidt, P., Reiss, A., Hanselmann, M., Stiefelhagen, R., and D\u00fcrichen, R. (2017, January 11\u201315). CNN-based sensor fusion techniques for multimodal human activity recognition. Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, HI, USA.","DOI":"10.1145\/3123021.3123046"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"137758","DOI":"10.1109\/ACCESS.2020.3012021","article-title":"Temporal-frequency attention-based human activity recognition using commercial WiFi devices","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.compbiomed.2017.12.025","article-title":"A general framework for sensor-based human activity recognition","volume":"95","author":"Shirahama","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"012027","DOI":"10.1088\/1742-6596\/1456\/1\/012027","article-title":"Classification methods performance on human activity recognition","volume":"1456","author":"Bustoni","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Kiprijanovska, I., Stankoski, S., Kalabakov, S., Broulidakis, J., Nduka, C., and Gjoreski, M. (2021). Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning. Activity and Behavior Computing, Springer.","DOI":"10.1007\/978-981-15-8944-7_10"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"R\u00f6ddiger, T., Wolffram, D., Laubenstein, D., Budde, M., and Beigl, M. (2019, January 9). Towards respiration rate monitoring using an in-ear headphone inertial measurement unit. Proceedings of the 1st International Workshop on Earable Computing, London, UK.","DOI":"10.1145\/3345615.3361130"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, M., Cho, J., Lee, S., and Jung, Y. (2019). IMU sensor-based hand gesture recognition for human-machine interfaces. Sensors, 19.","DOI":"10.3390\/s19183827"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Georgi, M., Amma, C., and Schultz, T. (2015). Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing. Biosignals, Citeseer.","DOI":"10.5220\/0005276900990108"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mummadi, C.K., Leo, F.P.P., Verma, K.D., Kasireddy, S., Scholl, P.M., Kempfle, J., and Laerhoven, K.V. (2018). Real-time and embedded detection of hand gestures with an IMU-based glove. Informatics, 5.","DOI":"10.3390\/informatics5020028"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kang, S.W., Choi, H., Park, H.I., Choi, B.G., Im, H., Shin, D., Jung, Y.G., Lee, J.Y., Park, H.W., and Park, S. (2017). The development of an IMU integrated clothes for postural monitoring using conductive yarn and interconnecting technology. Sensors, 17.","DOI":"10.3390\/s17112560"},{"key":"ref_51","unstructured":"Zhang, Q. (2019). Evaluation of a Wearable System for Motion Analysis during Different Exercises. [Master\u2019s Thesis, ING School of Industrial and Information Engineering]. Available online: https:\/\/www.politesi.polimi.it\/handle\/10589\/149029."},{"key":"ref_52","first-page":"1","article-title":"Stroke patients\u2019 acceptance of a smart garment for supporting upper extremity rehabilitation","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kim, H., Kang, Y., Valencia, D.R., and Kim, D. (February, January 31). An Integrated System for Gait Analysis Using FSRs and an IMU. Proceedings of the 2018 Second IEEE International Conference on Robotic Computing (IRC), Laguna Hills, CA, USA.","DOI":"10.1109\/IRC.2018.00073"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Abdulrahim, K., Hide, C., Moore, T., and Hill, C. (2010, January 14\u201315). Aiding MEMS IMU with building heading for indoor pedestrian navigation. Proceedings of the 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service, Kirkkonummi, Finland.","DOI":"10.1109\/UPINLBS.2010.5653986"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wahjudi, F., and Lin, F.J. (2019, January 15\u201318). IMU-Based Walking Workouts Recognition. Proceedings of the 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland.","DOI":"10.1109\/WF-IoT.2019.8767285"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Nagano, H., and Begg, R.K. (2018). Shoe-insole technology for injury prevention in walking. Sensors, 18.","DOI":"10.3390\/s18051468"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Cesareo, A., Previtali, Y., Biffi, E., and Aliverti, A. (2019). Assessment of breathing parameters using an inertial measurement unit (IMU)-based system. Sensors, 19.","DOI":"10.3390\/s19010088"},{"key":"ref_58","first-page":"1","article-title":"Deep inertial poser: Learning to reconstruct human pose from sparse inertial measurements in real time","volume":"37","author":"Huang","year":"2018","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Younas, J., Margarito, H., Bian, S., and Lukowicz, P. (2020, January 7\u20139). Finger Air Writing-Movement Reconstruction with Low-cost IMU Sensor. Proceedings of the MobiQuitous 2020\u201417th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Online.","DOI":"10.1145\/3448891.3448925"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"153287","DOI":"10.1109\/ACCESS.2019.2948102","article-title":"Wi-motion: A robust human activity recognition using WiFi signals","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Liu, X., Cao, J., Tang, S., and Wen, J. (2014, January 2\u20135). Wi-sleep: Contactless sleep monitoring via wifi signals. Proceedings of the 2014 IEEE Real-Time Systems Symposium, Rome, Italy.","DOI":"10.1109\/RTSS.2014.30"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"80058","DOI":"10.1109\/ACCESS.2019.2923743","article-title":"Joint activity recognition and indoor localization with WiFi fingerprints","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bahle, G., Fortes Rey, V., Bian, S., Bello, H., and Lukowicz, P. (2021). Using privacy respecting sound analysis to improve bluetooth based proximity detection for COVID-19 exposure tracing and social distancing. Sensors, 21.","DOI":"10.3390\/s21165604"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hossain, A.M., and Soh, W.S. (2007, January 3\u20137). A comprehensive study of bluetooth signal parameters for localization. Proceedings of the 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece.","DOI":"10.1109\/PIMRC.2007.4394215"},{"key":"ref_65","first-page":"1","article-title":"Real-time human motion behavior detection via CNN using mmWave radar","volume":"3","author":"Zhang","year":"2018","journal-title":"IEEE Sens. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, C.X., Wang, B., Chen, C., Xie, L., Wang, M., Trigoni, N., and Markham, A. (August, January 31). Heart rate sensing with a robot mounted mmwave radar. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197437"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Sun, Y., Hang, R., Li, Z., Jin, M., and Xu, K. (2019, January 1\u20134). Privacy-Preserving Fall Detection with Deep Learning on mmWave Radar Signal. Proceedings of the 2019 IEEE Visual Communications and Image Processing (VCIP), Sydney, Australia.","DOI":"10.1109\/VCIP47243.2019.8965661"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Li, Z., Lei, Z., Yan, A., Solovey, E., and Pahlavan, K. (2020, January 4\u20136). ThuMouse: A micro-gesture cursor input through mmWave radar-based interaction. Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE46568.2020.9043082"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cheng, Y., and Zhou, T. (2019, January 23\u201325). UWB indoor positioning algorithm based on TDOA technology. Proceedings of the 2019 10th International Conference on Information Technology in Medicine and Education (ITME), Qingdao, China.","DOI":"10.1109\/ITME.2019.00177"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13673-019-0168-7","article-title":"Random forest and WiFi fingerprint-based indoor location recognition system using smart watch","volume":"9","author":"Lee","year":"2019","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/MCOM.2003.1215641","article-title":"Ultra-wideband radio technology: Potential and challenges ahead","volume":"41","author":"Porcino","year":"2003","journal-title":"IEEE Commun. Mag."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.procs.2020.03.004","article-title":"Activity recognition in smart homes using UWB radars","volume":"170","author":"Bouchard","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"95","DOI":"10.4236\/jcc.2016.43015","article-title":"Algorithm for gesture recognition using an IR-UWB radar sensor","volume":"4","author":"Ren","year":"2016","journal-title":"J. Comput. Commun."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/JBHI.2020.3025900","article-title":"SleepPoseNet: Multi-view learning for sleep postural transition recognition using UWB","volume":"25","author":"Piriyajitakonkij","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"15","DOI":"10.2528\/PIERC18121407","article-title":"Effect of Limb Movements on Compact UWB Wearable Antenna Radiation Performance for Healthcare Monitoring","volume":"91","author":"Bharadwaj","year":"2019","journal-title":"Prog. Electromagn. Res."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Qi, J., and Liu, G.P. (2017). A robust high-accuracy ultrasound indoor positioning system based on a wireless sensor network. Sensors, 17.","DOI":"10.3390\/s17112554"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Hoeflinger, F., Saphala, A., Schott, D.J., Reindl, L.M., and Schindelhauer, C. (2019, January 6\u20137). Passive indoor-localization using echoes of ultrasound signals. Proceedings of the 2019 International Conference on Advanced Information Technologies (ICAIT), Yangon, Myanmar.","DOI":"10.1109\/AITC.2019.8921282"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/TNSRE.2018.2829913","article-title":"Towards wearable A-mode ultrasound sensing for real-time finger motion recognition","volume":"26","author":"Yang","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1109\/JSEN.2017.2647960","article-title":"BLUESOUND: A new resident identification sensor\u2014Using ultrasound array and BLE technology for smart home platform","volume":"17","author":"Mokhtari","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Rossi, M., Feese, S., Amft, O., Braune, N., Martis, S., and Tr\u00f6ster, G. (2013, January 18\u201322). AmbientSense: A real-time ambient sound recognition system for smartphones. Proceedings of the 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), San Diego, CA, USA.","DOI":"10.1109\/PerComW.2013.6529487"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.apacoust.2018.08.013","article-title":"An averaging method for accurately calibrating smartphone microphones for environmental noise measurement","volume":"143","author":"Garg","year":"2019","journal-title":"Appl. Acoust."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Thiel, B., Kloch, K., and Lukowicz, P. (2012, January 6). Sound-based proximity detection with mobile phones. Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones, Toronto, ON, Canada.","DOI":"10.1145\/2389148.2389152"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1109\/TPAMI.2006.197","article-title":"Activity recognition of assembly tasks using body-worn microphones and accelerometers","volume":"28","author":"Ward","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Murata, S., Yara, C., Kaneta, K., Ioroi, S., and Tanaka, H. (2014, January 10\u201312). Accurate indoor positioning system using near-ultrasonic sound from a smartphone. Proceedings of the 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, Oxford, UK.","DOI":"10.1109\/NGMAST.2014.17"},{"key":"ref_85","unstructured":"Rossi, M., Seiter, J., Amft, O., Buchmeier, S., and Tr\u00f6ster, G. (2013, January 7\u20138). RoomSense: An indoor positioning system for smartphones using active sound probing. Proceedings of the 4th Augmented Human International Conference, Stuttgart, Germany."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Sathyamoorthy, A.J., Patel, U., Savle, Y.A., Paul, M., and Manocha, D. (2020). COVID-robot: Monitoring social distancing constraints in crowded scenarios. arXiv.","DOI":"10.1371\/journal.pone.0259713"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Lee, Y.H., and Medioni, G. (2014, January 6\u201312). Wearable RGBD indoor navigation system for the blind. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-16199-0_35"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.3390\/s150101022","article-title":"Depth camera-based 3D hand gesture controls with immersive tactile feedback for natural mid-air gesture interactions","volume":"15","author":"Kim","year":"2015","journal-title":"Sensors"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Nagarkoti, A., Teotia, R., Mahale, A.K., and Das, P.K. (2019, January 23\u201327). Realtime indoor workout analysis using machine learning & computer vision. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856547"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"5731","DOI":"10.1007\/s11042-018-5738-6","article-title":"Multi-view depth-based pairwise feature learning for person-person interaction recognition","volume":"78","author":"Li","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Pancholi, S., and Agarwal, R. (2016, January 21\u201324). Development of low cost EMG data acquisition system for Arm Activities Recognition. Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India.","DOI":"10.1109\/ICACCI.2016.7732427"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"103653","DOI":"10.1016\/j.autcon.2021.103653","article-title":"ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors","volume":"126","author":"Bangaru","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TSMCA.2011.2116004","article-title":"A framework for hand gesture recognition based on accelerometer and EMG sensors","volume":"41","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern.-Part A Syst. Humans"},{"key":"ref_94","unstructured":"Kim, J., Mastnik, S., and Andr\u00e9, E. EMG-based hand gesture recognition for realtime biosignal interfacing. Proceedings of the 13th International Conference on Intelligent User Interfaces, Gran Canaria, Spain."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Benatti, S., Farella, E., and Benini, L. (2014, January 13\u201317). Towards EMG control interface for smart garments. Proceedings of the 2014 ACM International Symposium on Wearable Computers, Seattle, WA, USA.","DOI":"10.1145\/2641248.2641352"},{"key":"ref_96","first-page":"480","article-title":"EMG signal classification for human computer interaction: A review","volume":"33","author":"Ahsan","year":"2009","journal-title":"Eur. J. Sci. Res."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Jia, R., and Liu, B. (2013, January 5\u20138). Human daily activity recognition by fusing accelerometer and multi-lead ECG data. Proceedings of the 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013), KunMing, China.","DOI":"10.1109\/ICSPCC.2013.6664056"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Liu, J., Chen, J., Jiang, H., Jia, W., Lin, Q., and Wang, Z. (2018, January 27\u201330). Activity recognition in wearable ECG monitoring aided by accelerometer data. Proceedings of the 2018 IEEE international symposium on circuits and systems (ISCAS), Florence, Italy.","DOI":"10.1109\/ISCAS.2018.8351076"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yao, L., Zhang, D., Wang, X., Sheng, Q.Z., and Gu, T. (2017, January 7\u201310). Multi-person brain activity recognition via comprehensive EEG signal analysis. Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Melbourne, Australia.","DOI":"10.1145\/3144457.3144477"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Kaur, B., Singh, D., and Roy, P.P. (2017, January 15\u201316). Eyes open and eyes close activity recognition using EEG signals. Proceedings of the International Conference on Cognitive Computing and Information Processing, Bengaluru, India.","DOI":"10.1007\/978-981-10-9059-2_1"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Liu, Y., Sourina, O., and Nguyen, M.K. (2010, January 20\u201322). Real-time EEG-based human emotion recognition and visualization. Proceedings of the 2010 International Conference on Cyberworlds, Singapore.","DOI":"10.1109\/CW.2010.37"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Ishimaru, S., Kunze, K., Uema, Y., Kise, K., Inami, M., and Tanaka, K. (2014, January 13\u201317). Smarter eyewear: Using commercial EOG glasses for activity recognition. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, WA, USA.","DOI":"10.1145\/2638728.2638795"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.bspc.2018.05.011","article-title":"A dual model approach to EOG-based human activity recognition","volume":"45","author":"Lu","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"353","DOI":"10.2165\/00007256-198805060-00002","article-title":"Blood pressure behaviour during physical activity","volume":"5","author":"Palatini","year":"1988","journal-title":"Sport. Med."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Lu, K., Yang, L., Seoane, F., Abtahi, F., Forsman, M., and Lindecrantz, K. (2018). Fusion of heart rate, respiration and motion measurements from a wearable sensor system to enhance energy expenditure estimation. Sensors, 18.","DOI":"10.3390\/s18093092"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"284","DOI":"10.3389\/fnhum.2018.00284","article-title":"Improving real-life estimates of emotion based on heart rate: A perspective on taking metabolic heart rate into account","volume":"12","author":"Brouwer","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"012017","DOI":"10.1088\/1757-899X\/428\/1\/012017","article-title":"Sleep and wake classification based on heart rate and respiration rate","volume":"428","author":"Li","year":"2018","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_108","first-page":"1242","article-title":"Wearable devices for precision medicine and health state monitoring","volume":"66","author":"Bychkov","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Tateno, S., Guan, X., Cao, R., and Qu, Z. (2018, January 11\u201314). Development of drowsiness detection system based on respiration changes using heart rate monitoring. Proceedings of the 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Nara, Japan.","DOI":"10.23919\/SICE.2018.8492599"},{"key":"ref_110","first-page":"745","article-title":"A Lip-reading Algorithm Using Optical Flow and Properties of Articulatory Phonation","volume":"21","author":"Lee","year":"2018","journal-title":"J. Korea Multimed. Soc."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2016.06.092","article-title":"Parkinson\u2019s disease monitoring by biomechanical instability of phonation","volume":"255","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Benalc\u00e1zar, M.E., Motoche, C., Zea, J.A., Jaramillo, A.G., Anchundia, C.E., Zambrano, P., Segura, M., Palacios, F.B., and P\u00e9rez, M. (2017, January 16\u201320). Real-time hand gesture recognition using the Myo armband and muscle activity detection. Proceedings of the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, Ecuador.","DOI":"10.1109\/ETCM.2017.8247458"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1364\/JOT.85.000562","article-title":"Detection of physical stress using facial muscle activity","volume":"85","author":"Li","year":"2018","journal-title":"J. Opt. Technol."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Caulcrick, C., Russell, F., Wilson, S., Sawade, C., and Vaidyanathan, R. (2018, January 26\u201329). Unilateral Inertial and Muscle Activity Sensor Fusion for Gait Cycle Progress Estimation. Proceedings of the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Enschede, The Netherlands.","DOI":"10.1109\/BIOROB.2018.8487936"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Fasih Haider, P.A., and Luz, S. (2020, January 16-20). Automatic Recognition of Low-Back Chronic Pain Level and Protective Movement Behaviour using Physical and Muscle Activity Information. Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), Buenos Aires, Argentina.","DOI":"10.1109\/FG47880.2020.00065"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, C., Hudson, S.E., Harrison, C., and Sample, A. (2018, January 21\u201326). Wall++ room-scale interactive and context-aware sensing. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada.","DOI":"10.1145\/3173574.3173847"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Cheng, J., Amft, O., and Lukowicz, P. (2010, January 17\u201320). Active capacitive sensing: Exploring a new wearable sensing modality for activity recognition. Proceedings of the International Conference on Pervasive Computing, Helsinki, Finland.","DOI":"10.1007\/978-3-642-12654-3_19"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Laput, G., and Harrison, C. (2017, January 6\u201311). Electrick: Low-cost touch sensing using electric field tomography. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA.","DOI":"10.1145\/3025453.3025842"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Valtonen, M., Maentausta, J., and Vanhala, J. (2009, January 9\u201313). Tiletrack: Capacitive human tracking using floor tiles. Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications, Galveston, TX, USA.","DOI":"10.1109\/PERCOM.2009.4912749"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.jneumeth.2016.12.007","article-title":"Use of electric field sensors for recording respiration, heart rate, and stereotyped motor behaviors in the rodent home cage","volume":"277","author":"Noble","year":"2017","journal-title":"J. Neurosci. Methods"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"8441","DOI":"10.1109\/JSEN.2021.3049273","article-title":"Multi-Features Capacitive Hand Gesture Recognition Sensor: A Machine Learning Approach","volume":"21","author":"Wong","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Chen, K.Y., Lyons, K., White, S., and Patel, S. (2013, January 8\u201311). uTrack: 3D input using two magnetic sensors. Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, St. Andrews, UK.","DOI":"10.1145\/2501988.2502035"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3161162","article-title":"Synchrowatch: One-handed synchronous smartwatch gestures using correlation and magnetic sensing","volume":"1","author":"Reyes","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Lyons, K. (2016, January 12\u201316). 2D input for virtual reality enclosures with magnetic field sensing. Proceedings of the 2016 ACM International Symposium on Wearable Computers, Heidelberg, Germany.","DOI":"10.1145\/2971763.2971787"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Pirkl, G., and Lukowicz, P. (2012, January 5\u20138). Robust, low cost indoor positioning using magnetic resonant coupling. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370281"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3369831","article-title":"Auraring: Precise electromagnetic finger tracking","volume":"3","author":"Parizi","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_127","first-page":"1","article-title":"IM6D: Magnetic tracking system with 6-DOF passive markers for dexterous 3D interaction and motion","volume":"34","author":"Huang","year":"2015","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Bian, S., Zhou, B., and Lukowicz, P. (2020). Social distance monitor with a wearable magnetic field proximity sensor. Sensors, 20.","DOI":"10.3390\/s20185101"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Bian, S., Zhou, B., Bello, H., and Lukowicz, P. (2020, January 12\u201316). A wearable magnetic field based proximity sensing system for monitoring COVID-19 social distancing. Proceedings of the 2020 International Symposium on Wearable Computers, Virtual Event.","DOI":"10.1145\/3410531.3414313"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MPRV.2020.3021321","article-title":"Wearables to fight COVID-19: From symptom tracking to contact tracing","volume":"19","author":"Amft","year":"2020","journal-title":"IEEE Pervasive Comput."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Bian, S., Hevesi, P., Christensen, L., and Lukowicz, P. (2021). Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments. Sensors, 21.","DOI":"10.3390\/s21062218"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/s10055-005-0005-3","article-title":"Neural network-based calibration of electromagnetic tracking systems","volume":"9","author":"Kindratenko","year":"2005","journal-title":"Virtual Real."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1109\/TITB.2009.2038904","article-title":"In-shoe plantar pressure measurement and analysis system based on fabric pressure sensing array","volume":"14","author":"Shu","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.pmcj.2016.08.015","article-title":"Measuring muscle activities during gym exercises with textile pressure mapping sensors","volume":"38","author":"Zhou","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_135","unstructured":"Kaddoura, Y., King, J., and Helal, A. (2005, January 4\u20136). Cost-precision tradeoffs in unencumbered floor-based indoor location tracking. Proceedings of the Third International Conference On Smart Homes and Health Telematic (ICOST), Montreal, QC, Canada."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Nakane, H., Toyama, J., and Kudo, M. (2011, January 8\u201310). Fatigue detection using a pressure sensor chair. Proceedings of the 2011 IEEE International Conference on Granular Computing, Taiwan, China.","DOI":"10.1109\/GRC.2011.6122646"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1123\/jsr.2017-0152","article-title":"Validating center-of-pressure balance measurements using the MatScan\u00ae pressure mat","volume":"27","author":"Goetschius","year":"2018","journal-title":"J. Sport Rehabil."},{"key":"ref_138","unstructured":"Aliau Bonet, C., and Pall\u00e0s Areny, R. (2012, January 9\u201314). A fast method to estimate body capacitance to ground. Proceedings of the Proceedings of XX IMEKO World Congress 2012, Busan, Korea."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1109\/TIM.2013.2258240","article-title":"A novel method to estimate body capacitance to ground at mid frequencies","volume":"62","year":"2013","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TIM.2006.880293","article-title":"Measurement and modeling mutual capacitance of electrical wiring and humans","volume":"55","author":"Buller","year":"2006","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Bian, S., and Lukowicz, P. (2021, January 25\u201326). A Systematic Study of the Influence of Various User Specific and Environmental Factors on Wearable Human Body Capacitance Sensing. Proceedings of the EAI International Conference on Body Area Networks, Virtual Event.","DOI":"10.1007\/978-3-030-95593-9_20"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1111\/jdv.13707","article-title":"Ambient humidity and the skin: The impact of air humidity in healthy and diseased states","volume":"30","author":"Goad","year":"2016","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1034\/j.1600-0846.2002.00351.x","article-title":"Effect of exposure of human skin to a dry environment","volume":"8","author":"Egawa","year":"2002","journal-title":"Skin Res. Technol."},{"key":"ref_144","unstructured":"Jonassen, N. (1998, January 6\u20138). Human body capacitance: Static or dynamic concept? [ESD]. Proceedings of the Electrical Overstress\/Electrostatic Discharge Symposium Proceedings 1998 (Cat. No. 98TH8347), Reno, NV, USA."},{"key":"ref_145","unstructured":"Bian, S., Yuan, S., Rey, V.F., and Lukowicz, P. (2021, January 20\u201322). Using human body capacitance sensing to monitor leg motion dominated activities with a wrist worn device. Proceedings of the Sensor-and Video-Based Activity and Behavior Computing: 3rd International Conference on Activity and Behavior Computing (ABC 2021), Virtual Event."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Cohn, G., Morris, D., Patel, S., and Tan, D. (2012, January 5\u201310). Humantenna: Using the body as an antenna for real-time whole-body interaction. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Austin, TX, USA.","DOI":"10.1145\/2207676.2208330"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Bian, S., Rey, V.F., Younas, J., and Lukowicz, P. (2019, January 11\u201315). Wrist-Worn Capacitive Sensor for Activity and Physical Collaboration Recognition. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan.","DOI":"10.1109\/PERCOMW.2019.8730581"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Bian, S., and Lukowicz, P. Capacitive Sensing Based On-board Hand Gesture Recognition with TinyML. Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, Virtual, 21\u201326 September 2021.","DOI":"10.1145\/3460418.3479287"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Cohn, G., Gupta, S., Lee, T.J., Morris, D., Smith, J.R., Reynolds, M.S., Tan, D.S., and Patel, S.N. (2012, January 5\u20138). An ultra-low-power human body motion sensor using static electric field sensing. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370233"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Pouryazdan, A., Prance, R.J., Prance, H., and Roggen, D. (2016, January 12\u201316). Wearable electric potential sensing: A new modality sensing hair touch and restless leg movement. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2968286"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"von Wilmsdorff, J., Kirchbuchner, F., Fu, B., Braun, A., and Kuijper, A. (2017, January 26\u201328). An exploratory study on electric field sensing. Proceedings of the European Conference on Ambient Intelligence, Malaga, Spain.","DOI":"10.1007\/978-3-319-56997-0_20"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Bian, S., Rey, V.F., Hevesi, P., and Lukowicz, P. (2019, January 11\u201315). Passive Capacitive based Approach for Full Body Gym Workout Recognition and Counting. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kyoto, Japan.","DOI":"10.1109\/PERCOM.2019.8767393"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Yang, D., Xu, B., Rao, K., and Sheng, W. (2018). Passive infrared (PIR)-based indoor position tracking for smart homes using accessibility maps and a-star algorithm. Sensors, 18.","DOI":"10.3390\/s18020332"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.infrared.2013.12.020","article-title":"A novel low-cost and small-size human tracking system with pyroelectric infrared sensor mesh network","volume":"63","author":"Yang","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_155","unstructured":"Kashimoto, Y., Hata, K., Suwa, H., Fujimoto, M., Arakawa, Y., Shigezumi, T., Komiya, K., Konishi, K., and Yasumoto, K. (December, January 28). Low-cost and device-free activity recognition system with energy harvesting PIR and door sensors. Proceedings of the Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services, Hiroshima, Japan."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Kashimoto, Y., Fujiwara, M., Fujimoto, M., Suwa, H., Arakawa, Y., and Yasumoto, K. (2017, January 27\u201329). ALPAS: Analog-PIR-sensor-based activity recognition system in smarthome. Proceedings of the 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), Taiwan, China.","DOI":"10.1109\/AINA.2017.33"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Naik, K., Pandit, T., Naik, N., and Shah, P. (2021). Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging. Sensors, 21.","DOI":"10.37247\/PASen.2.2021.5"},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Hossen, J., Jacobs, E.L., and Chowdhury, F.K. (2015, January 9\u201312). Activity recognition in thermal infrared video. Proceedings of the SoutheastCon 2015, Fort Lauderdale, FL, USA.","DOI":"10.1109\/SECON.2015.7132922"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1515\/hukin-2015-0116","article-title":"The use of thermal imaging in the evaluation of the symmetry of muscle activity in various types of exercises (symmetrical and asymmetrical)","volume":"49","author":"Chudecka","year":"2015","journal-title":"J. Hum. Kinet."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"586","DOI":"10.3844\/ajeassp.2011.586.597","article-title":"An evaluation of thermal imaging based respiration rate monitoring in children","volume":"4","author":"Saatchi","year":"2011","journal-title":"Am. J. Eng. Appl. Sci."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Ruminski, J., and Kwasniewska, A. (2017). Evaluation of respiration rate using thermal imaging in mobile conditions. Application of Infrared to Biomedical Sciences, Springer.","DOI":"10.1007\/978-981-10-3147-2_18"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Uddin, M.Z., and Torresen, J. (2018, January 19\u201320). A deep learning-based human activity recognition in darkness. Proceedings of the 2018 Colour and Visual Computing Symposium (CVCS), Gjovik, Norway.","DOI":"10.1109\/CVCS.2018.8496641"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"106534","DOI":"10.1016\/j.dib.2020.106534","article-title":"A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments","volume":"33","author":"Almazari","year":"2020","journal-title":"Data Brief"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"154935","DOI":"10.1109\/ACCESS.2019.2947024","article-title":"Wiar: A public dataset for wifi-based activity recognition","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_165","first-page":"27","article-title":"UWB-HA4D-1.0: An Ultra-wideband Radar Human Activity 4D Imaging Dataset","volume":"11","author":"Tian","year":"2022","journal-title":"Lei Da Xue Bao"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Delamare, M., Duval, F., and Boutteau, R. (2020). A new dataset of people flow in an industrial site with uwb and motion capture systems. Sensors, 20.","DOI":"10.3390\/s20164511"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-021-00876-0","article-title":"UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors","volume":"8","author":"Ahmed","year":"2021","journal-title":"Sci. Data"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Singh, A.D., Sandha, S.S., Garcia, L., and Srivastava, M. (2019, January 25). Radhar: Human activity recognition from point clouds generated through a millimeter-wave radar. Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems, Los Cabos, Mexico.","DOI":"10.1145\/3349624.3356768"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/JIOT.2021.3098338","article-title":"M-gesture: Person-independent real-time in-air gesture recognition using commodity millimeter wave radar","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_170","unstructured":"Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., and Natsev, P. (2017). The kinetics human action video dataset. arXiv."},{"key":"ref_171","unstructured":"Soomro, K., Zamir, A.R., and Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.cviu.2013.01.013","article-title":"A survey of video datasets for human action and activity recognition","volume":"117","author":"Chaquet","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Mohino-Herranz, I., Gil-Pita, R., Rosa-Zurera, M., and Seoane, F. (2019). Activity recognition using wearable physiological measurements: Selection of features from a comprehensive literature study. Sensors, 19.","DOI":"10.3390\/s19245524"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s00779-011-0415-z","article-title":"Personalization and user verification in wearable systems using biometric walking patterns","volume":"16","author":"Casale","year":"2012","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Hanley, D., Faustino, A.B., Zelman, S.D., Degenhardt, D.A., and Bretl, T. (2017, January 18\u201321). MagPIE: A dataset for indoor positioning with magnetic anomalies. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115961"},{"key":"ref_177","unstructured":"zhaxidelebsz (2022, April 30). Gym Workouts Data Set. Available online: https:\/\/github.com\/zhaxidele\/Toolkit-for-HBC-sensing."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Pouyan, M.B., Birjandtalab, J., Heydarzadeh, M., Nourani, M., and Ostadabbas, S. (2017, January 16\u201319). A pressure map dataset for posture and subject analytics. Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA.","DOI":"10.1109\/BHI.2017.7897206"},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Chatzaki, C., Skaramagkas, V., Tachos, N., Christodoulakis, G., Maniadi, E., Kefalopoulou, Z., Fotiadis, D.I., and Tsiknakis, M. (2021). The smart-insole dataset: Gait analysis using wearable sensors with a focus on elderly and Parkinson\u2019s patients. Sensors, 21.","DOI":"10.3390\/s21082821"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"3281","DOI":"10.1109\/JSEN.2014.2388153","article-title":"A Kalman Filter-Based Framework for Enhanced Sensor Fusion","volume":"15","author":"Assa","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"112675","DOI":"10.1016\/j.bios.2020.112675","article-title":"Pt-poly(L-lactic acid) microelectrode-based microsensor for in situ glucose detection in sweat","volume":"170","author":"Han","year":"2020","journal-title":"Biosens. Bioelectron."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"765987","DOI":"10.3389\/fbioe.2021.765987","article-title":"Recent Progress in Intelligent Wearable Sensors for Health Monitoring and Wound Healing Based on Biofluids","volume":"9","author":"Cheng","year":"2021","journal-title":"J. Front. Bioeng. Biotechnol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4596\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:34:13Z","timestamp":1760139253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,17]]},"references-count":182,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22124596"],"URL":"https:\/\/doi.org\/10.3390\/s22124596","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,17]]}}}