在没有特殊测试的情况下估计中风患者的身体能力

A. Derungs, J. Seiter, C. Schuster-Amft, O. Amft
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引用次数: 7

摘要

我们使用日常活动中获得的惯性传感器测量来估计卒中后患者的扩展Barthel指数(EBI),而不是具体评估。EBI是一项标准的临床评估,显示患者在处理日常任务方面的独立性。我们的工作旨在为患者和治疗师提供一个持续的能力评估,可以在没有专家监督的情况下使用。我们使用不需要基于数据的训练的基本规则从连续传感器数据中提取9个活动原语(AP),包括坐、站、过渡等。使用活动原语的相对持续时间,我们使用两种回归方法评估EBI评分估计:广义线性模型(GLM)和支持向量回归(SVR)。我们通过对11名中风患者在日间护理中心进行102天的日间康复的全天研究记录来评估我们的方法。我们的结果表明,对于所有研究参与者,使用SVR可以从活动原语估计EBI,平均相对误差约为12%。我们的研究结果表明,EBI可以在日常生活活动中进行评估,从而支持患者和治疗师跟踪康复进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating physical ability of stroke patients without specific tests
We estimate the Extended Barthel Index (EBI) in patients after stroke using inertial sensor measurements acquired during daily activity, rather than specific assessments. The EBI is a standard clinical assessment showing patient independence in handling everyday tasks. Our work aims at providing a continuous ability estimate for patients and therapists that could be used without expert supervision. We extract nine activity primitives (AP), including sitting, standing, transition, etc. from the continuous sensor data using basic rules that do not require data-based training. Using the relative duration of activity primitives, we evaluate the EBI score estimation using two regression methods: Generalised Linear Models (GLM) and Support-Vector Regression (SVR). We evaluated our approaches in full-day study recordings from 11 stroke patients with totally 102 days in ambulatory rehabilitation in a day-care centre. Our results show that EBI can be estimated from the activity primitives with approximately 12% relative error on average for all study participants using SVR. Our results indicate that EBI can be estimated in daily life activity, thus supporting patients and therapists in tracking rehab progress.
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