机器人辅助步态训练中步行性能的评估：一种基于经验数据的新方法。

Assessment of walking performance in robot-assisted gait training: a novel approach based on empirical data.

作者信息

Banz Raphael, Riener Robert, Lünenburger Lars, Bolliger Marc

机构信息

Balgrist University Hospital, Spinal Cord Injury Research, Zurich, Switzerland.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1977-80. doi: 10.1109/IEMBS.2008.4649576.

DOI:10.1109/IEMBS.2008.4649576

PMID:19163079

Abstract

Motivation and voluntary drive of patients can be improved by applying biofeedback during robot-assisted rehabilitation trainings. Biofeedback systems were traditionally based on theoretical assumptions. In this paper, we present a novel approach to calculate biofeedback during robot-assisted gait training. Our method was based on empirical data that were obtained from healthy subjects when simulating distinctive degrees of walking performance during robot-assisted gait training. This empirical data-based biofeedback (EDBF) method was evaluated with 18 subjects without gait disorders. A higher correlation between the subjects' walking performance and biofeedback values was found for the EDBF method compared to a theory-based biofeedback approach.

摘要

在机器人辅助康复训练中应用生物反馈可以提高患者的积极性和自主驱动力。传统上，生物反馈系统是基于理论假设的。在本文中，我们提出了一种在机器人辅助步态训练期间计算生物反馈的新方法。我们的方法基于在机器人辅助步态训练期间模拟不同程度步行表现时从健康受试者获得的经验数据。这种基于经验数据的生物反馈（EDBF）方法在18名无步态障碍的受试者中进行了评估。与基于理论的生物反馈方法相比，EDBF方法在受试者的步行表现和生物反馈值之间发现了更高的相关性。