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
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