B1237
Title: Stroke rehabilitation assessment by using wrist-worn sensors
Authors: Xi Chen - Newcastle University (United Kingdom) [presenting]
Abstract: 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 at a high frequency. To address this issue, based on wearable sensing, we developed two systems that can predict the assessment score in an objective manner. With wrist-worn sensors, accelerometer data were 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 sets of new features based on the wavelet domain, 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 employed the longitudinal mixed-effects model with Gaussian process prior (LMGP) to evaluate the patient's recovery levels. Comprehensive experiments were conducted to evaluate our systems on stroke patients, and the results suggested its effectiveness.
