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Updated: Jun 3, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Published on: April 12, 2016

为人工智能支持的手语识别提供三电曲传感器.

Wei Wang¹, Xiangkun Bo¹, Weilu Li¹

¹Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|January 8, 2025

概括

本研究介绍了一种使用 triboelectric 传感器和人工智能 (AI) 来识别手语的自动供电可穿戴系统. 该系统达到96.15%的精度,克服了传统动力传感器的局限性.

关键词:

长期短期记忆长期短期记忆模式识别模式识别智能可穿戴系统是一个智能可穿戴系统.电阻曲传感器是电阻曲传感器.

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科学领域:

可穿戴电子设备的电子产品
人与机器接口人与机器接口
传感器和执行器

背景情况:

当代可穿戴传感器需要外部电源,这限制了它们在物联网中的应用.
自动供电的传感器对于先进的人机交互和各种可穿戴应用至关重要.

研究的目的:

开发一种智能可穿戴系统,用于使用自动供电的 triboelectric 传感器识别手语.
将人工智能 (AI) 集成到可穿戴电子产品中,以实现准确的信号模式识别.

主要方法:

开发了一个带有五个形结构的自动供电 triboelectric 传感器和数据采集单元的系统.
采用滑动旋机制进行定量传感器性能评估.
使用长短期记忆 (LSTM) 网络在降噪后进行手语信号模式识别.

主要成果:

使用两个训练有素的LSTM模型,实现了96.15%的手语识别准确率.
通过低通波器成功降低了环境噪音和传感器通道之间的交叉通话.
证明了 triboelectric 传感器的定量性能评估.

结论:

这项工作介绍了 triboelectric 传感器与人工智能的新集成,以有效识别手语.
开发的自动供电系统为可穿戴电子产品中的 triboelectric 传感器提供了新的应用.
该系统克服了传统可穿戴传感器的功率限制,从而实现了更广泛的部署.