Low Power Radar Sensors and TinyML for Embedded Gesture Recognition and Non-Contact Vital Sign Monitoring
Michele MAGNO, Head of the Project-based learning Center, ETH Zurich, D-ITET
Human-computer interface (HCI) is an attractive scenario, and a wide range of solutions, strategies, and technologies have been proposed recently. A promising novel sensing technology is high-frequency short-range Doppler-radar. This talk presents a low-power high-accuracy embedded hand-gesture recognition using low power short-range radar sensors from Acconeer. A 2D Convolutional Neural Network (CNN) using range frequency Doppler features is combined with a Temporal Convolutional Neural Network (TCN) for time sequence prediction. The final algorithm has a model size of only 45723 parameters, yielding a memory footprint of only 91kB. We acquired two datasets containing 11 challenging hand gestures performed by 26 different people containing a total of 20210 gesture instances. The algorithm achieved an accuracy of up to 92% on the 11 hands gestures. Furthermore, we implemented the prediction algorithm on the GAP8 Parallel Ultra-Low-Power processor RISC-V and ARM Cortex-M processors. The hardware-software solution matches the requirements for battery-operated wearable devices. This work will also present novel recent results based on neural network with Transformes and a demostrator of a form factor of an ear bud will be presented.
This year I will present also some recent resent on the use of the same radar sensor technology to extract vital sign monitoring such as respiration rate and especially the challenging hearth rate combining signal processing and tinyML. The Algorithm run in a few hundreds of kilobyte footprint and can run in a ARM Cortex-M microcontroller.
