腕戴设备实时识别跑步脚型，助跑者即时纠正不良姿势。
StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs
- 用轻量级神经网络处理腕部传感器的原始信号
- 6比特量化后模型每推理仅耗电0.35微焦，延迟0.14毫秒
- 支持连续运行13.6天，适合无云依赖的运动场景
跑步具有显著健康益处，但不正确的步态模式可能导致受伤，尤其缺乏专业反馈时。已有基于摄像头、鞋垫或体穿戴传感器的步态分析系统，但多笨重且仅支持离线分析。腕戴可穿戴设备更实用且非侵入，但实现实时步态识别仍面临惯性测量单元（IMU）信号噪声大、算力有限及依赖云端等挑战。本文提出StrikeWatch，一种完全在设备端运行的紧凑型腕戴系统，利用IMU信号实现实时步态识别。以检测足跟与前脚掌着地为例，帮助跑者通过视听反馈在跑步中自我修正不良步态。设计四种轻量级深度学习架构（1D-CNN、1D-SepCNN、LSTM、Transformer），并针对两种典型嵌入式FPGA（AMD Spartan-7 XC7S15和Lattice iCE40UP5K）进行能效优化。通过自建硬件原型采集户外跑步标注数据集，并使用全自动部署流程评估所有模型。结果揭示模型复杂度与硬件效率间的权衡。在12名参与者上测试，6比特量化后的1D-SepCNN达到平均F1分数0.847，每推理耗电仅0.350微焦，延迟0.140毫秒（iCE40UP5K，20 MHz），支持320 mAh电池下连续运行13.6天。所有数据集与代码已开源于GitHub。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。