AI-Powered Noncontact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing

• Published in: IEEE internet of things journal Vol. 10; no. 11; pp. 9465 - 9481
• Main Authors: Abedi, Hajar, Ansariyan, Ahmad, Morita, Plinio P., Wong, Alexander, Boger, Jennifer, Shaker, George
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Activity recognition; Artificial neural networks; autonomous systems; Biomedical monitoring; Cloud computing; Deep learning; Gait; gait monitoring; Gait recognition; Internet of Things; Legged locomotion; Machine learning; Millimeter waves; mm-wave radar; Monitoring; Radar; Radar equipment; Radar signal processing; Real time; sequential deep learning; Signal processing; Signal processing algorithms; Washrooms
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3235268

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.