Sazonov Edward S, Fulk George, Sazonova Nadezhda, Schuckers Stephanie
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13676, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2200-3. doi: 10.1109/IEMBS.2009.5334908.
Stroke is the leading cause of disability in the United States. It is estimated that 700,000 people in the United States will experience a stroke each year and that there are over 5 million Americans living with a stroke. In this paper we describe a novel methodology for automatic recognition of postures and activities in patients with stroke that may be used to provide behavioral enhancing feedback to patients with stroke as part of a rehabilitation program and potentially enhance rehabilitation outcomes. The recognition methodology is based on Support Vector classification of the sensor data provided by a wearable shoe-based device. The proposed methodology was validated in a case study involving an individual with a chronic stroke with impaired motor function of the affected lower extremity and impaired walking ability. The results suggest that recognition of postures and activities may be performed with very high accuracy.
中风是美国致残的主要原因。据估计，美国每年有70万人会中风，且有超过500万美国人患有中风。在本文中，我们描述了一种用于自动识别中风患者姿势和活动的新方法，该方法可作为康复计划的一部分，用于向中风患者提供行为增强反馈，并有可能改善康复效果。该识别方法基于对一种基于可穿戴鞋类设备提供的传感器数据进行支持向量分类。所提出的方法在一个案例研究中得到了验证，该案例涉及一名患有慢性中风的个体，其受影响下肢运动功能受损且行走能力受损。结果表明，姿势和活动的识别可以非常高精度地进行。
