Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we developed an automated system that can predict the assessment score in an objective manner. With wrist-worn sensors, accelerometer data was collected from 59 stroke survivors in free-living environments for a duration of 8 weeks, and we aim to map the week-wise accelerometer data (3 days per week) to the assessment score by developing signal processing and predictive model pipeline. To achieve this, we proposed two types of new features, which can encode the rehabilitation information from both paralysed/non-paralysed sides while suppressing the high-level noises such as irrelevant daily activities. Based on the proposed features, we further developed the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks). Comprehensive experiments were conducted to evaluate our system on both acute and chronic patients, and the results suggested its effectiveness.
翻译:然而,传统的中风康复评估方法(如流行临床评估)可能是主观和昂贵的,而且病人也不太方便以高频率到诊所看病。为了解决这个问题,我们在基于可磨损的遥感和机器学习技术的这项工作中开发了一个自动化系统,可以客观地预测评估得分。用手腕式传感器,从59个中风幸存者免费生活环境中收集了8周的加速计数据,我们的目标是通过开发信号处理和预测模式管道,将每周的加速计数据(每星期3天)映射到评估得分上。为了实现这一目标,我们提出了两类新特征,这些特征可以将来自瘫痪/不连续两侧的康复信息编码起来,同时抑制诸如无关的日常活动等高层次的噪音。根据拟议特征,我们进一步开发了高斯进程之前的长度混合效应模型(LMGP),该模型可以模拟由不同主题和不同时间段导致的随机效应,并且建议对病人进行的时间段8进行长期效果评估。