tinyRadar: mmWave Radar based Human Activity Classification for Edge Computing

• Published in: IEEE International Symposium on Circuits and Systems proceedings pp. 2414 - 2417
• Main Authors: Yadav, Satyapreet Singh, Agarwal, Radha, Bharath, Kola, Rao, Sandeep, Thakur, Chetan Singh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.05.2022
• Subjects: CMSIS-NN; CNN; Computational modeling; Computer architecture; Convolutional neural networks; Data privacy; Edge computing; Millimeter wave communication; Millimeter wave radar; Real-time systems; TI mmWave radar IWR6843; tinyML; velocity-time map
• ISSN: 2158-1525
• DOI: 10.1109/ISCAS48785.2022.9937293

The rising need for elderly care, child care, and intrusion detection challenges the sustainability of traditional systems that depend on in-person monitoring and surveillance. The current state-of-the-art technology heavily relies on InfraRed (IR) and camera-based systems, which often require cloud computing. It can lead to higher latency, data theft, and privacy issues of being continuously monitored. This paper proposes a novel tiny-ML-based single-chip radar solution for on-edge sensing and detection of human activity. Edge computing within a small form factor solves the issue of data theft and privacy concerns as radar provides point cloud information. Also, it can operate in adverse environmental conditions like fog, dust, and low light. This work used the Texas Instruments IWR6843 millimeter wave (mmWave) radar board to implement signal processing and Convolutional Neural Network (CNN) for human activity classification. A dataset for four different human activities generalized over six subjects was collected to train the 8-bit quantized CNN model. The real-time inference engine implemented on Cortex®-R4F using CMSIS-NN framework has a model size of 1.44 KB, gives the classification result after every 120 ms, and has an overall subject-independent accuracy of 96.43%.