电网独立的机器学习辅助可穿戴步态分析设备,带有 triboelectric-electromagnetic混合动力能源收割机
Yoonsang Ra1, Dongjun Kim2, Mi Song Nam3
1Department of Mechanical Engineering, Kyung Hee University, Yongin, 17104, South Korea; Department of Mechanical Engineering (Integrated Engineering Program), Kyung Hee University, Yongin, 17104, South Korea; School of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, South Korea.
Biosensors & bioelectronics
|July 15, 2025
概括
本研究介绍了一种电网独立的机器学习辅助可穿戴设备用于步态分析 (EMWG),该设备使用人工智能进行准确的步态分析,并使用混合能源收获器进行自动供电,从而实现下一代可穿戴技术.
科学领域:
- 可穿戴技术是可穿戴的技术.
- 生物机械能源采集 生物机械能源采集
- 医疗保健中的人工智能
背景情况:
- 步态分析对于诊断神经和肌肉骨疾病至关重要.
- 现有的可穿戴步态分析系统通常依赖于外部电源,这限制了它们的实用性.
- 开发自动供电,智能可穿戴设备是运动分析的一个关键挑战.
研究的目的:
- 开发一个电网独立的机器学习辅助可穿戴设备用于步行分析 (EMWG).
- 整合一台混合能源收割机来为嵌入AI的微控制器提供动力.
- 为了证明该设备在现实世界的应用中具有的能力,例如醉酒者的确定.
主要方法:
- 多层感知子 (MLP) 被选为最佳的人工智能 (AI) 算法用于步态分析.
- 一个 triboelectric-electromagnetic (TENG-EMG) 混合能源收割机被设计用于收获生物机械能量.
- 该EMWG设备被集成到智能鞋子中进行概念验证测试.
主要成果:
- 在步态分析中,MLP模型实现了88%的准确性和84.22%的模型转换率.
- 混合动力能源收割机在运行过程中产生了相当大的功率 (高达594.75微瓦).
- 该系统展示了26.45%的功率效率,并成功地识别了88%的精度的醉酒步行.
结论:
- 该EMWG提供了一个可行的解决方案,用于独立于电网的AI驱动的可穿戴步态分析.
- 混合能源采集方法有效地为嵌入式AI微控制器提供动力.
- 这项技术作为先进的基础,自我维持的运动分析可穿戴设备.
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