Exploring Automatic Gym Workouts Recognition Locally on Wearable Resource-Constrained Devices

Abstract

Automatic gym activity recognition on energy-and resource-constrained wearable devices removes the human-interaction requirement during intense gym sessions - like soft-touch tapping and swiping. This work presents a tiny and highly accurate residual convolutional neural network that runs in milliwatt microcontrollers for automatic workouts classification. We evaluated the inference performance of the deep model with quantization on three resource-constrained devices: two microcontrollers with ARM-Cortex M4 and M7 core from ST Microelectronics, and a GAP8 system on chip, which is an open-sourced, multi-core RISC-V computing platform from Green-Waves Technologies. Experimental results show an accuracy of up to 90.4% for eleven workouts recognition with full precision inference. The paper also presents the trade-off performance of the resource-constrained system. While keeping the recognition accuracy (88.1%) with minimal loss, each inference takes only 3.2 ms on GAP8, benefiting from the 8 RISC-V cluster cores. We measured that it features an execution time that is 18.9x and 6.5x faster than the Cortex-M4 and Cortex-M7 cores, showing the feasibility of real-time on-board workouts recognition based on the described data set with 20 Hz sampling rate. The energy consumed for each inference on GAP8 is 0.41 mJ compared to 5.17 mJ on Cortex-M4 and 8.07 mJ on Cortex-M7 with the maximum clock. It can lead to longer battery life when the system is battery-operated. We also introduced an open data set composed of fifty sessions of eleven gym workouts collected from ten subjects that is publicly available.