从惯性测量单元 (IMU) 与ResUNet之间的肢体步行轨迹同步
概括
这项研究引入了新的自编码器网络,用于在步行康复期间同步四肢运动. 该系统在预测肢体间轨迹方面表现出高准确度,改善了步行康复策略.
科学领域:
- 生物医学工程 生物医学工程
- 康复机器人 康复机器人
- 机器学习用于医疗保健
背景情况:
- 对于患有神经元损伤的患者来说,步行康复至关重要,这会显著影响生活质量.
- 目前的康复经常忽视四肢协调的作用,将上肢和步行训练分开.
- 肢体间协调是人类步态生理学的基础.
研究的目的:
- 开发和评估基于自动编码器的网络 (ResNetAE,ResUNet) 进行肢体间步行轨迹同步.
- 通过同步上肢和下肢运动,使步行康复系统能够积极控制.
- 通过采用整体,肢体间的方法来提高步行康复的有效性.
主要方法:
- 收集了30名健康受试者在三个年龄组 (年轻人,中年人,老年人) 的步态数据.
- 使用Xsens MVN惯性测量单元 (IMU) 系统提取和同步身体部分位置.
- 使用带有残余块的自编码网络 (ResNetAE,ResUNet) 来使用手和脚坐标数据预测肢体间的轨迹.
主要成果:
- 在预测四肢间轨迹时,达到0.0472米的平均绝对误差 (MAE).
- 证明了高相同步,相同步指数 (PSI) 为96.70%.
- 在未见对象的准确性和同步性方面超过了LSTMAE基线.
结论:
- 拟议的ResNetAE和ResUNet模型显示出对肢体间步行轨迹同步的显著前景.
- 该系统的性能足以推动肢体间相位同步研究.
- 这些发现支持了肢体间意识系统在步行康复外骨架技术中的潜在整合.
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