tinyML Talks India: tinyRadar: mmWave Radar-based Human Activity Classification for Edge Computing
“tinyRadar: mmWave Radar-based Human Activity Classification for Edge Computing”
Radha Agarwal
Master’s student
Indian Institute of Science, Bangalore
Most of the current systems for patient monitoring, elderly, and child care are camera-based and often require cloud computing. But, camera-based systems pose a privacy risk, and cloud computing can lead to higher latency, data theft, and connectivity issues. Why face these challenges in the current era of intelligent sensing modalities with tiny and edge solutions?
This talk will give insights about a tinyML-based single-chip radar solution for on-edge sensing and detection of the environment. Thus, the hassle can be avoided by using the tiny radar, which protects privacy, and works in all weather and lighting conditions while sensing with a contactless interface. At the same time, edge computing on it gives a small form factor that makes it robust enough for remote deployments. This end-to-end pipeline from sensing to detection is demonstrated for real-time human activity classification using a Texas Instruments IWR6843 millimeter-wave radar board. The edge implementation of the 8-bit quantized inference engine is done on the radar’s Cortex-R4F MCU using the CMSIS-NN custom APIs.
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