Non-Contact Smart Sensing of Physical Activities during Quarantine Period Using SDR Technology

被引:7
|
作者
Khan, Muhammad Bilal [1 ,2 ]
Mustafa, Ali [2 ]
Rehman, Mubashir [3 ]
AbuAli, Najah Abed [3 ]
Yuan, Chang [1 ]
Yang, Xiaodong [1 ]
Shah, Fiaz Hussain [1 ]
Abbasi, Qammer H. [4 ]
机构
[1] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
[2] COMSATS Univ Islamabad, Dept Elect & Comp Engn, Attock Campus, Attock 43600, Pakistan
[3] United Arab Emirates Univ UAEU, Coll Informat Technol, Abu Dhabi 15551, U Arab Emirates
[4] Univ Glasgow, Sch Engn, Glasgow G12 8QQ, Lanark, Scotland
关键词
COVID-19; smart sensing; OFDM; SDR; WCSI; RECOGNITION; DEVICE; SYSTEM;
D O I
10.3390/s22041348
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Non-contact Evaluation of Concrete Structures Using Air-coupled Sensing Technique
    ZHU Jinying Department of CivilArchitectural and Environmental EngineeringThe University of Texas at Austin University StationCAustinTexas USA
    结构工程师, 2011, 27(S1) (S1) : 313 - 319
  • [42] Non-contact physical stress measurement using thermal imaging and blind source separation
    Hong, Kan
    OPTICAL REVIEW, 2020, 27 (01) : 116 - 125
  • [43] Non-contact Dynamic Displacement and Vibration Measuring Technology using Laser Doppler Vibrometer
    Zhu, Hui
    Asundi, A. K.
    Ting, Liu
    Song Yunfeng
    FOURTH INTERNATIONAL CONFERENCE ON EXPERIMENTAL MECHANICS, 2010, 7522
  • [44] Precision stage using a non-contact planar actuator based on magnetic suspension technology
    Jung, KS
    Baek, YS
    MECHATRONICS, 2003, 13 (8-9) : 981 - 999
  • [45] Non-contact physical stress measurement using thermal imaging and blind source separation
    Kan Hong
    Optical Review, 2020, 27 : 116 - 125
  • [46] Rapid measurement of physical properties of cheddar cheese using a non-contact ultrasound technique
    Cho, B
    Irudayaraj, J
    Bhardwaj, MC
    TRANSACTIONS OF THE ASAE, 2001, 44 (06): : 1759 - 1762
  • [47] Technology development of the non-contact type oil leaks sensor using ASK modulation
    Sugai Y.
    Hara U.
    Inoue A.
    Hashida S.
    IEEJ Transactions on Sensors and Micromachines, 2011, 131 (11) : 402 - 403
  • [48] A non-contact lie detector using Radar Vital Signs Monitor (RVSM) technology
    Geisheimer, J
    Greneker, EF
    IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE, 2001, 16 (08) : 10 - 14
  • [49] High-precision non-contact current measurement technology using magnetic sensors
    Yamagishi K.
    Ikeda K.
    Nakazawa K.
    IEEJ Transactions on Sensors and Micromachines, 2017, 137 (08) : 223 - 228
  • [50] Detection of Breathing Sounds During Sleep Using Non-Contact Audio Recordings
    Rosenwein, T.
    Dafna, E.
    Tarasiuk, A.
    Zigel, Y.
    2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2014, : 1489 - 1492