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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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一个基于数组的可穿戴系统,用于ASL手势识别.

Prashanth Jonna, Madhav Rao

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    概括
    此摘要是机器生成的。

    使用磁力计的新可穿戴系统准确地对美国手语 (ASL) 的静态手势进行分类. 这种具有成本效益的方法可以帮助听力障碍的人,通过使手部阻塞最小,强大的手语识别.

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    科学领域:

    • 机器人和人机交互的人机交互
    • 生物医学工程 生物医学工程
    • 信号处理 信号处理

    背景情况:

    • 手的手势分类对于手语识别 (SLR) 系统至关重要,帮助有听力和语言障碍的人.
    • 现有的方法,如基于表面的电肌图 (sEMG),惯性测量单元 (IMU),柔性传感器和基于视频的系统,具有包括计算强度,体积或限制用户运动在内的局限性.
    • 对于美国手语 (ASL) 识别的高度歧视性,非侵入性和成本效益高的解决方案的需求是显著的.

    研究的目的:

    • 为ASL识别提出一种新的,精确保存的静态手势分类系统.
    • 为SLR开发一个具有成本效益和最小侵入性的可穿戴系统.
    • 评估拟议系统的准确性和稳定性.

    主要方法:

    • 开发一种可穿戴系统,使用一系列磁力计进行静态手势分类.
    • 实施K-最近邻国 (KNN) 分类模型来处理磁力计数据.
    • 设计考虑的重点是尽量减少手上的电子覆盖,并优化系统成本.

    主要成果:

    • 实现了98.60%的平均准确度来分类ASL字母.
    • 在ASL数字的分类中达到94.07%的平均准确率.
    • 证明了强大的分类结果,该系统在用户手上占用了微不足道的空间.

    结论:

    • 拟议的基于磁力计阵列的可穿戴系统为ASL识别提供了具有成本效益,可靠和强大的解决方案.
    • 该系统的非侵入性设计和高精度使其适合实际采用.
    • 这种方法解决了现有的SLR技术的局限性,为听力障碍者社区提供了改进的沟通工具铺平了道路.