Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low-power. Energy allocation is essential for motion-based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra-low-power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real-world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices.

中文翻译：

---

具有动能收集功能的可穿戴设备活动识别的能量分配

从身体运动中获取动能被认为是一种很有前途的低功耗可穿戴设备的可充电能源。能量分配对于基于运动的可充电设备至关重要，因为从运动中获得的能量变化很大。基于超低功耗可穿戴设备的现实特征和我们的测量观察，我们提出了优化框架分配能量以最大化人类活动识别的平均准确度，并分别提供离线和在线算法。我们使用真实世界的人类活动和动能收集数据集来评估提议的能量分配方法。实验结果验证了我们提出的能量分配方法可以最大化能量分配效用并提高可穿戴设备的能源效率。