准确的COP轨迹估计在健康和病态的步态使用多式仪器内和深度学习模型
概括
这项研究提出了一种新的深度学习方法,使用负担得起的内传感器来准确估计行走时的压力中心 (COP) 轨迹. 这一突破提供了一种可访问的工具,用于在实验室之外监测神经系统疾病的步态和平衡.
科学领域:
- 生物力学 生物力学
- 神经学 神经学
- 机器学习 机器学习
背景情况:
- 在实验室环境之外评估压力中心 (COP) 轨迹对于了解神经系统疾病中的步态和平衡变化至关重要.
- 当前的COP测量工具往往昂贵且难以获得,限制了广泛的临床应用.
- 需要为动态COP轨迹估计提供具有成本效益和便携性的解决方案.
研究的目的:
- 引入和验证一种新的深度循环神经网络 (DRNN) 模型,用于估计动态COP轨迹.
- 合并来自负担得起的,异质的内底传感器的数据,包括强度敏感电阻 (FSR) 和惯性测量单元 (IMU).
- 评估拟议方法在实验室外的门诊任务中的技术和融合有效性.
主要方法:
- 开发一个DRNN模型,集成来自八个细胞FSR阵列和嵌入内的IMU的数据.
- 在模拟的真实世界行走任务中与黄金标准设备 (例如强力板) 进行验证.
- 对中侧 (ML) 和前后 (AP) 方向的根平均平方误差 (RMSE) 的分析,用于健康和神经肌肉状况组.
主要成果:
- 该DRNN模型准确地估计了ML (0.51-0.59厘米) 和AP (1.44-1.53厘米) 方向的低RMSE的动态COP轨迹.
- 在健康个体和患有神经肌肉疾病的个体中,技术有效性得到证实.
- 在神经肌肉组中,COP衍生的指标与临床测量 ambulatory 功能和下肢强度的临床测量显示出显著的相关性.
结论:
- 拟议的方法提供了一种技术上有效和潜在的成本效益的方法,用于使用内传感器估计COP轨迹.
- 这项技术对临床应用具有前景,使得在神经疾病中远程监测步态和平衡.
- 这些发现支持基于内的COP估计用于评估功能性移动性的合有效性.
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