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 and continues 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 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. 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.

中文翻译：

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在自由生活环境中使用可穿戴加速度计进行自动中风康复评估

中风被认为是一个主要的全球健康问题，对于中风幸存者来说，监测康复水平是关键。然而，传统的中风康复评估方法（如流行的临床评估）具有主观性和成本高的特点，而且患者频繁就诊也不太方便。为了解决这个问题，在这项基于可穿戴传感和机器学习技术的工作中，我们开发了一个自动化系统，可以客观且持续地预测评估分数。使用腕戴式传感器，从 59 名在自由生活环境中的中风幸存者收集加速度计数据，持续 8 周，我们的目标是通过开发信号将每周加速度计数据（每周 3 天）映射到评估分数处理和预测模型管道。为了达成这个，我们提出了两个新特征，可以对来自瘫痪/非瘫痪双方的康复信息进行编码，同时抑制不相关的日常活动等高级噪声。我们进一步开发了具有高斯过程先验（LMGP）的纵向混合效应模型，该模型可以模拟由不同主题和时间段（在 8 周内）引起的随机效应。进行了综合实验以评估我们的系统在急性和慢性患者身上的效果，结果表明其有效性。它可以模拟由不同科目和时间段（在 8 周内）引起的随机效应。进行了综合实验以评估我们的系统在急性和慢性患者身上的效果，结果表明其有效性。它可以模拟由不同科目和时间段（在 8 周内）引起的随机效应。进行了综合实验以评估我们的系统在急性和慢性患者身上的效果，结果表明其有效性。