Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers

Chen, Xi1; Guan, Yu2; Shi, Jian Qing3; Du, Xiu-Li4; Eyre, Janet5

2023-02-01

10.1007/s42486-022-00124-3

CCF Transactions on Pervasive Computing and Interaction   影响因子和分区

2524-521X

2524-5228

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.

Wrist-worn accelerometer sensor Stroke rehabilitation CAHAI score Regression model

ESCI ; EI

英语

其他

National Natural Science Foundation of China[12271239] ; Shenzhen Fundamental Research Program[20220111]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications

WOS:000928591100001

SPRINGERNATURE

20230613565827

Automation ; Learning systems ; Random processes ; Regression analysis ; Signal processing ; Wearable sensors

Information Theory and Signal Processing:716.1 ; Automatic Control Principles and Applications:731 ; Probability Theory:922.1 ; Mathematical Statistics:922.2 ; Mechanical Instruments:943.1

Web of Science

1.Hainan Rural Credit Union, Hainan, Peoples R China
2.Univ Warwick, Dept Comp Sci, Coventry, England
3.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen, Peoples R China
4.Nanjing Normal Univ, Sch Math Sci, Nanjing, Peoples R China
5.Newcastle Univ, Inst Neurosci, Newcastle Upon Tyne, England

Chen, Xi,Guan, Yu,Shi, Jian Qing,et al. Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers[J]. CCF Transactions on Pervasive Computing and Interaction,2023,5(2):206-225.

Chen, Xi,Guan, Yu,Shi, Jian Qing,Du, Xiu-Li,&Eyre, Janet.(2023).Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers.CCF Transactions on Pervasive Computing and Interaction,5(2),206-225.

Chen, Xi,et al."Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers".CCF Transactions on Pervasive Computing and Interaction 5.2(2023):206-225.