基于压电能量采集器的同步能量采集与步态识别
Simultaneous Energy Harvesting and Gait Recognition using Piezoelectric Energy Harvester
摘要：
压电能量采集器通过应力或振动产生电力，作为延长可穿戴设备电池寿命的可行解决方案，正受到越来越多的关注。最近的研究进一步表明，除了产生能量外，PEH还可以作为一种无源传感器来有效地检测人类步态，因为其应力或振动模式受到步态的显著影响。然而，由于PEH不是为精确测量运动而设计的，因此可实现的步态识别精度对于传统的分类算法仍然很低。当同时存储所产生的电力时，精度进一步恶化。为了对步态进行可靠的分类，同时存储产生的能量，我们做出了两个不同的贡献。首先，我们提出了一种预处理算法，以过滤掉能量存储对PEH电信号的影响。其次，我们提出了一种基于长短期记忆（LSTM）网络的分类器，以准确捕获步态诱导发电中的时间信息。我们原型的鞋垫的形状因子的步态识别架构，并评估其步态识别以及能量收集性能与20名受试者。我们的研究结果表明，与最先进的架构相比，所提出的架构检测人类步态的召回率提高了12%，并收获了高达127%的能量，同时消耗的功率减少了38%。
英文摘要：
Piezoelectric energy harvester, which generates electricity from stress or vibrations, is gaining increasing attention as a viable solution to extend battery life in wearables. Recent research further reveals that, besides generating energy, PEH can also serve as a passive sensor to detect human gait power-efficiently because its stress or vibration patterns are significantly influenced by the gait. However, as PEHs are not designed for precise measurement of motion, achievable gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. To classify gait reliably while simultaneously storing generated energy, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signal. Second, we propose a long short-term memory (LSTM) network-based classifier to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12% higher recall and harvests up to 127% more energy while consuming 38% less power compared to the state-of-the-art.
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