  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 develop an automated
system that can predict the assessment score in an objective manner. With
wrist-worn sensors, accelerometer data is collected from 59 stroke survivors in
free-living environments for a duration of 8 weeks, and we 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 propose
two types of new features, which can encode the rehabilitation information from
both paralysed and non-paralysed sides while suppressing the high level noises
such as irrelevant daily activities. Based on the proposed features, we further
develop 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 are conducted to
evaluate our system on both acute and chronic patients, and the promising
results suggest its effectiveness.
