可解释的人工智能用于从多式联运数据融合的步行速度分析
Abdullah Alharthi1, Abdulrahman Al Ayidh1, Ahmed Alqurashi2
1Department of Electrical Engineering, King Khalid University, Abha, Saudi Arabia.
PloS one
|February 27, 2026
概括
这项研究提出了一个新的深度学习框架,用于使用多式联络数据融合准确的步行速度分类. 多流四极管CNN模型实现了卓越的性能,为各种应用提供了强大的工具.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 步行速度分析对于医疗保健,康复和人机交互至关重要.
- 现有的方法在准确捕捉复杂的步态动态方面面临挑战.
研究的目的:
- 开发一个先进的框架,使用多式联运数据融合和深度学习进行步行速度分类.
- 使用层级相关性传播 (LRP) 来优化框架,以增强功能选择和模型稳定性.
主要方法:
- 集成的全身运动捕捉,电肌图 (EMG) 和力板数据来自50名成年人在4个数据集.
- 提出并对新型深度学习架构 (CNN+LSTM,多流CNN) 与TCN,变压器,GRU和统计分类器进行基准测试.
- 采用了三个不同的交叉验证策略,包括基于主题的分割,以确保严格的模型评估.
主要成果:
- 多流四极管CNN模型在所有实验中获得了最高的F1分数,高达98%.
- 与传统的统计分类器和其他深度学习模型相比,该框架表现优越.
- 层级相关性传播 (LRP) 成功识别了关键特征,通过扰动分析验证了模型的稳定性和耐用性.
结论:
- 拟议的多式联运数据融合和深度学习框架为步行速度分类提供了高度准确和弹性解决方案.
- 由LRP驱动的功能优化可以提高模型的性能和可解释性.
- 这种先进的工具在医疗保健,康复和自主系统中具有很大的应用潜力.
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