tinyRadar: mmWave Radar-based Human Activity Classification for Edge Computing
This work proposes a novel tinyML-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. We used the Texas Instruments IWR6843 millimeter wave (mmWave) radar board to implement signal processing chain on DSP C67x and Convolutional Neural Network (CNN) on Cortex®-R4F MCU for real-time inference of human activity. 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 1.44KB, gives the classification result after every 120ms, and has an overall subject-independent accuracy of 96.43%. The proposed architecture can be used for various applications by retraining the CNN model.
Paper link: tinyRadar: mmWave Radar-based Human Activity Classification for Edge Computing