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Article overview
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Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments | | Xi Chen
; Yu Guan
; Jian-Qing Shi
; Xiu-Li Du
; Janet Eyre
; | | Date: |
17 Sep 2020 | | 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 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. | | Source: | arXiv, 2009.08798 | | Services: | Forum | Review | PDF | Favorites | |
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