A Compact Spiking Neural Network for Real-Time Swimming Style Recognition on Low-Power FPGA

Accurate and energy-efficient recognition of swimming styles is essential for real-time training analysis in wearable systems. This work introduces an end-to-end, spike-driven approach for swimming style classification, in which a Spiking Neural Network (SNN) operates on event-based inertial inputs. To the best of our knowledge, this is the first application of neuromorphic computing to swimming style recognition.

The signal encoding employs delta modulation on accelerometer, gyroscope, and magnetometer axes, converting continuous sensor streams into sparse binary spike trains that capture temporal dynamics of swimming movements without requiring explicit filtering or feature extraction. A compact fully connected SNN with leaky integrate-and-fire neurons classifies four swimming styles (freestyle, breaststroke, backstroke, and butterfly) using only 18,176 trainable parameters.

The approach is validated on a publicly available benchmark of 40 swimmers recorded in realistic training conditions, achieving an F1 of 0.945 under Leave-One-Subject-Out cross-validation.

To support deployment on resource-constrained wearable devices, we implement an optimized hardware architecture on a low-power Lattice iCE40-UltraPlus FPGA. The event-driven nature of the SNN enables efficient inference by processing only active spikes, reducing power consumption while maintaining low latency. Performance evaluations indicate an execution time of 7.8 ms per classification, with energy usage of 93.6 uJ, demonstrating the feasibility of on-device real-time swimming analysis.