Parnandi Avinash, Wade Eric, Mataric Maja
Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:86-9. doi: 10.1109/IEMBS.2010.5626156.
We present an approach to wearable sensor-based assessment of motor function in individuals post stroke. We make use of one on-body inertial measurement unit (IMU) to automate the functional ability (FA) scoring of the Wolf Motor Function Test (WMFT). WMFT is an assessment instrument used to determine the functional motor capabilities of individuals post stroke. It is comprised of 17 tasks, 15 of which are rated according to performance time and quality of motion. We present signal processing and machine learning tools to estimate the WMFT FA scores of the 15 tasks using IMU data. We treat this as a classification problem in multidimensional feature space and use a supervised learning approach.
我们提出了一种基于可穿戴传感器的中风后个体运动功能评估方法。我们利用一个身体上的惯性测量单元（IMU）来自动进行Wolf运动功能测试（WMFT）的功能能力（FA）评分。WMFT是一种用于确定中风后个体运动功能能力的评估工具。它由17项任务组成，其中15项根据执行时间和运动质量进行评分。我们提出了信号处理和机器学习工具，以使用IMU数据估计这15项任务的WMFT FA评分。我们将此视为多维特征空间中的分类问题，并使用监督学习方法。
