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

Abstract

The elderly population is growing, and the health care 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 non-invasive sensing systems. This paper makes use of Radio Frequency sensing, where signal propagation is observed in Channel State Information (CSI) reports on Activities of Daily Living (ADLs). Real-time data has 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 classification for the real-time monitoring system allowing detection of abnormalities in the expected ADLs of the elderly. The timing of classifications gives insight 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 milliseconds.

A Comparison of Machine Learning Algorithms for Wi-Fi Sensing Using CSI Data

“…Also, the increasing elderly population and the strain on healthcare services due to the COVID-19 pandemic have led to a demand for technological solutions in elderly homes. Research [11] introduced a real-time, noninvasive sensing system that utilized radio frequency (RF) sensing and channel state information (CSI) reports to monitor activities of daily living (ADLs). Machine learning, specifically the random forest algorithm, was employed to accurately classify ADL categories like "movement", "empty room", and "no activity", which achieved 100% accuracy on new testing data.…”

“…It also explores the behavior of various ML algorithms, i.e., SL and DL, when a CSI-based dataset is presented to these ML algorithms for training and testing. Taylor et al [11] Real-Time Activity Sensing Activity Sensing Identification of optimal machine learning techniques.…”