以实例为基础的学习与实时比例肌肉控制的原型减少:一项随机用户研究,证明了对假肢嵌入式系统的精度保护数据减少.
Tim Sziburis1,2, Markus Nowak3, Davide Brunelli4
1Institute for Neuroinformatics (INI), Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany. tim.sziburis@alumni.cern.
Medical & biological engineering & computing
|October 5, 2023
概括
与回归技术相比,k-近邻 (kNN) 方法在假肢控制手势检测方面表现优越. 使用决策表面映射 (DSM) 减少数据集,保持高准确度,显著减少可穿戴设备的计算负载.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 康复技术 康复技术 康复技术
背景情况:
- 手势检测对于先进的假肢控制至关重要.
- 基于实例的学习的高计算需求阻碍了便携式设备中的实时应用.
- 数据集缩小技术对于高效的嵌入式控制系统至关重要.
研究的目的:
- 设计,实施和验证基于kNN的学习技术,用于假肢控制中的手势检测.
- 评估数据集缩小方法,用于电池驱动设备的实时确定性.
- 将kNN与决策面映射 (DSM) 的性能与回归技术进行比较.
主要方法:
- 使用八通道sEMG腕带进行数据采集.
- 评估了数据集缩小技术,重点是针对kNN的决策表面映射 (DSM).
- 进行线下交叉验证和实时试点实验 (在线目标实现测试).
- 进行了一项随机的双盲用户研究,将kNN (带有和没有DSM) 与回归 (RR) 和随机里叶特征 (RR-RFF) 的RR进行比较.
主要成果:
- 基于kNN的方法显著优于回归技术 ().
- 与标准kNN相比,DSM-kNN实现了超过99%的数据集减少,成功率没有统计学上显著的差异.
- 对于kNN和DSM-kNN的运行时间复杂性与使用最佳参数的原始样本大小变得线性 ([公式:参见文本]).
结论:
- 基于kNN的学习技术,特别是DSM数据集缩小技术,对于实时假肢手势检测非常有效.
- DSM显著降低了计算负载,使得可靠的整合到可穿戴假肢设备.
- 开发的方法为先进的假肢控制系统提供了有前途,高效和准确的解决方案.
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