In this work, we present a cloud-based system for noncontact, real-time recognition, and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition, and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a data set of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network [gated recurrent unit (GRU)] model selected for real-time deployment due to its balance of speed and accuracy compared to 2-D convolutional neural network long short-term memory (2D-CNNLSTM) and long short-term memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject's activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.
Abedi, H., Ansariyan, A., Morita, P. P., Wong, A., Boger, J., & Shaker, G. (2023). AI-Powered Noncontact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing. IEEE Internet of Things Journal, 10(11), 9465–9481. https://doi.org/10.1109/JIOT.2023.3235268