StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs

TL;DR

StrikeWatch is a low-power, on-device wrist-worn gait recognition system using compact deep learning models optimized for FPGA hardware, enabling real-time feedback for runners to correct gait patterns.

Contribution

The paper introduces four energy-efficient deep learning architectures optimized for FPGA deployment, enabling real-time gait recognition on wrist-worn devices without cloud reliance.

Findings

1D-SepCNN achieves 0.847 F1 score with 0.350 microjoule per inference.

The system supports up to 13.6 days of continuous operation on a 320 mAh battery.

Hardware-optimized models balance accuracy and energy efficiency effectively.

Abstract

Running offers substantial health benefits, but improper gait patterns can lead to injuries, particularly without expert feedback. While prior gait analysis systems based on cameras, insoles, or body-mounted sensors have demonstrated effectiveness, they are often bulky and limited to offline, post-run analysis. Wrist-worn wearables offer a more practical and non-intrusive alternative, yet enabling real-time gait recognition on such devices remains challenging due to noisy Inertial Measurement Unit (IMU) signals, limited computing resources, and dependence on cloud connectivity. This paper introduces StrikeWatch, a compact wrist-worn system that performs entirely on-device, real-time gait recognition using IMU signals. As a case study, we target the detection of heel versus forefoot strikes to enable runners to self-correct harmful gait patterns through visual and auditory feedback during running. We propose four compact DL architectures (1D-CNN, 1D-SepCNN, LSTM, and Transformer) and optimize them for energy-efficient inference on two representative embedded Field-Programmable Gate Arrays (FPGAs): the AMD Spartan-7 XC7S15 and the Lattice iCE40UP5K. Using our custom-built hardware prototype, we collect a labeled dataset from outdoor running sessions and evaluate all models via a fully automated deployment pipeline. Our results reveal clear trade-offs between model complexity and hardware efficiency. Evaluated across 12 participants, 6-bit quantized 1D-SepCNN achieves the highest average F1 score of 0.847 while consuming just 0.350 microjoule per inference with a latency of 0.140 ms on the iCE40UP5K running at 20 MHz. This configuration supports up to 13.6 days of continuous inference on a 320 mAh battery. All datasets and code are available in the GitHub repository https://github.com/tianheng-ling/StrikeWatch.