Algorithms and Tiny ML
Energy-efficient algorithms and Tiny Machine Learning, including learning on embedded low-power devices
- Gesture recognition with Radars
- TinyTCN
- FANN-on-MCU
- MCU evaluation for TinyML
- Smart Agriculture and Parallel Tiny ML
Exploring Automatic Gym Workouts Recognition Locally on Wearable Resource-Constrained Devices
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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.