步行速度对轨迹预测的作用,使用深度学习模型用于外骨应用
Rania Kolaghassi1, Gianluca Marcelli1, Konstantinos Sirlantzis2
1School of Engineering, University of Kent, Canterbury CT2 7NT, UK.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
完全连接的神经网络 (FCNNs) 可以在训练的速度范围内预测步行轨迹. 在训练数据之外的速度下,性能下降,突出了外骨控制的局限性.
科学领域:
- 生物力学 生物力学
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 步行速度显著影响生物力学模式和关节动力学.
- 准确预测步行轨迹对于高级应用,如外骨控制至关重要.
研究的目的:
- 为了评估完全连接的神经网络 (FCNNs) 在预测人类步行轨迹的有效性,跨越广泛的步行速度.
- 评估不同FCNN模型 (通用,低速,高速,低高速) 的性能,以预测步态动力学,特别是部,膝盖和脚的角度.
- 确定FCNN在训练数据范围内外的速度的预测能力,与实时外骨架适应相关.
主要方法:
- 收集了22名健康成年人的步态数据,他们以28速 (0.5-1.85米/秒) 步行.
- 开发和评估了四种完全连接的神经网络模型.
- 使用短期 (一步前进) 和长期 (200 步时间) 递归预测评估预测性能,测量平均绝对误差 (MAE).
主要成果:
- 专门的低速和高速模型在排除速度时显示出显著的性能退化 (43.7%-90.7%的MAE增加).
- 在排除中速时测试时,低高速模型表现出更好的性能 (2.8%短期,9.8%长期).
- 对于训练范围内的速度,FCNN显示了插值能力,但对于该范围以外的速度,准确性降低了.
结论:
- 完全连接的神经网络可以在测试的速度范围内插入步态预测.
- 对于超出或低于训练数据边界的步行速度,FCNNs的预测精度会降低.
- 模型选择和训练数据范围是可靠的FCNN应用在辅助设备的动态步态预测的关键因素.
关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.外骨是外骨的组成部分.超值推算的方法预测 预测 预测 预测步态 步态 步态 步态步行速度增加了步行速度.动力学是动力学.预测 预测 预测 预测更多相关视频
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