用轻量级算法实时检测动作变化，省电超67%。
An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
- 基于动态模板匹配的无训练非参数算法，每步仅16kFLOPs。
- UCA-EHAR上98%敏感度，WISDM上97%敏感度，误触发率低。
- 无需预定义动作类别，适配智能眼镜、手表等设备。
运行在惯性测量单元（IMUs）上的可穿戴设备在持续进行人体活动识别（HAR）时，即使长时间保持相同动作也会消耗大量能量。为此，本文提出一种轻量级变化检测门控机制：基于动态模板匹配的非参数算法，每步计算仅需约16kFLOPs，无需离线训练，也不依赖预先定义的目标动作类别。该门控仅在检测到动作变化时才调用完整HAR网络，在真实监测场景下将计算负载降低超过67%。算法在智能眼镜、智能手表和智能手机数据上均通过验证，仅需短暂设备校准。在UCA-EHAR数据集上达到98%敏感度，确保不遗漏真实动作转换；75%特异性有效控制不必要的HAR调用。在WISDM数据集上实现97%敏感度与76%特异性，展现出对多种场景的鲁棒性与灵活性。
原文摘要 · Abstract (English)
Wearable devices running Human Activity Recognition(HAR) on Inertial Measurement Units~(IMUs) waste energy by performing continuous classification for each window, even during long periods of unchanged activity. We address this with a lightweight change-detection gate: a non-parametric algorithm based on dynamic template matching that runs continuously at only approximately 16kFLOPs per step, requires no offline training, and does not need prior definition of target activity classes. The gate invokes the full HAR network only when it detects an activity change, reducing the computational load by over 67% in realistic monitoring settings. The algorithm is evaluated on smart glasses, smartwatch, and smartphone data, requiring only a brief device-specific calibration phase. The gate achieves 98% sensitivity on UCA-EHAR, ensuring no genuine activity transition is missed, while 75% specificity keeps unnecessary HAR invocations low. Results on WISDM are 97% sensitivity and 76% specificity, demonstrating robustness and flexibility to various settings.
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