Edgar S, Swyka Timothy, Fulk George, Sazonov Edward S
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13676, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3772-5. doi: 10.1109/IEMBS.2010.5627577.
Regaining the ability to walk after a stroke is a major rehabilitation goal. Rehabilitation strategies that are task oriented and intensive can drive cortical reorganization and increase activity levels in people after a stroke. This paper describes a novel, wearable device for use with such rehabilitation strategies. The device is based on the combination of a smartphone and in-shoe sensors, and is designed to operate in free living conditions. Data collected from the device can be used for automatic recognition of postures and activities, characterization of extremity use and to provide behavioral enhancing feedback to patients recovering from a stroke. The proposed wearable platform's operation was validated in a small scale study involving three healthy individuals. The average accuracy of classification of three postures and activities was over 99%. Based on the results of validation and previously reported results on recognition of postures and activities in stroke patients, it is anticipated that recognition of postures and activities may be performed with high accuracy in free living conditions.
中风后恢复行走能力是一项主要的康复目标。以任务为导向且高强度的康复策略能够推动中风患者的大脑皮层重组并提高其活动水平。本文介绍了一种适用于此类康复策略的新型可穿戴设备。该设备基于智能手机和鞋内传感器的组合,设计用于在自由生活环境中运行。从该设备收集的数据可用于自动识别姿势和活动、表征肢体使用情况,并为中风康复患者提供行为增强反馈。所提出的可穿戴平台的操作在一项涉及三名健康个体的小规模研究中得到了验证。三种姿势和活动的分类平均准确率超过99%。基于验证结果以及先前报道的中风患者姿势和活动识别结果,可以预期在自由生活环境中姿势和活动的识别能够高精度地进行。