步态
高斯过程
弹道
克里金
计算机科学
自回归模型
人工智能
高斯分布
步态参数对能量消耗的影响
过程(计算)
控制理论(社会学)
步态分析
数学
机器学习
统计
物理
生物
天文
操作系统
量子力学
生理学
控制(管理)
作者
Jisoo Hong,Changmook Chun,Seung-Jong Kim,Frank C. Park
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering
[Institute of Electrical and Electronics Engineers]
日期:2019-06-01
卷期号:27 (6): 1236-1245
被引量:22
标识
DOI:10.1109/tnsre.2019.2914095
摘要
This paper proposes a Gaussian process-based method for trajectory learning and generation of individualized gait motions at arbitrary user-designated walking speeds, intended to be used in generating reference motions for robotic gait rehabilitation systems. We utilize a nonlinear dimension reduction technique based on Gaussian process dynamical models (GPDMs), in which the internal dynamics is modeled as a second-order Markov process evolving in a lower-dimensional latent space. After the GPDM parameters are identified with training data obtained from gait motions of healthy subjects walking at different speeds, our method then employs Gaussian process regression (GPR) to predict the initial two states of the latent space dynamics from any arbitrary desired walking speed and the anthropometric parameters of the test subject. Motions are then generated by directly mapping the latent space dynamics to joint trajectories. Experimental studies involving more than 100 subjects indicate that our method generates gait patterns with 30% less mean square prediction errors compared to recent state-of-the-art methods, while also allowing for arbitrary user-specified walking speeds.
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