  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 GreenWaves
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.
