tinyRadar: LSTM-based Real-time Multi-target Human Activity Recognition for Edge Computing

Wireless Human Activity Recognition (HAR) has emerged as a vital technology with wide-ranging applications, including healthcare, aged care, and child monitoring. Radar-based HAR systems, grounded in electromagnetic principles, offer resilience to lighting variations and uphold user privacy by efficiently processing sparse point cloud data. These systems demonstrate robust performance even in obstructed environments. Nonetheless, existing radar-based HAR methods face a limitation in relying on fixed time windows for data classification. This approach may not be the most adaptable, especially when monitoring individuals of various ages, from children to the elderly, who perform activities at different speeds. This paper introduces "tinyRadar," a novel system that capitalizes on the capabilities of the Texas Instruments IWR6843 radar for sensing and the Raspberry Pi 4 for executing Long Short-Term Memory (LSTM) inference. tinyRadar is trained on activities of varying durations, enabling it to cater to different human activity speeds. Remarkably, tinyRadar achieves 93% real-time inference accuracy in recognizing eight distinct activity classes, classifying each activity frame within 10 ms, with a compact model size of 311 KB.