An Implementation of Real-Time Activity Sensing Using Wi-Fi: Identifying Optimal Machine-Learning Techniques for Performance Evaluation

The elderly population is growing, and the healthcare system is experiencing a strain on services provided to the elderly. The recent COVID-19 pandemic has increased this strain and has resulted in an increased risk of exposure during visits to elderly homes, increasing the desire to provide technological solutions to counteract this. Currently, there lack reliable real-time noninvasive sensing systems. This article makes use of radio-frequency (RF) sensing, where signal propagation is observed in channel state information (CSI) reports on activities of daily living (ADLs). Real-time data have been collected for three classifications: “movement,” “empty room,” and “no activity,” A filter is applied to reduce the noise of the CSI data. Then the mean, max, min, kurtosis, skew, and standard deviation features are extracted from the CSI data. A machine-learning model provides a classification for the real-time monitoring system allowing the detection of abnormalities in the expected ADLs of the elderly. The timing of classifications gives insights into the real-time capabilities of the system. The random forest algorithm is chosen to create the machine-learning model based on accuracy and timing capabilities. The model was able to achieve an accuracy of 100% on new unseen testing data with an average classification time of 7.31 ms.