Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation

Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stroke rehabilitation. Classifying movements using non-intrusive wearable sensors provides context for arm use and is essential for the development of a home rehabilitation system. However, the movement c...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-12, Vol.24 (1), p.210
Main Authors: Oh, Youngmin, Choi, Sol-A, Shin, Yumi, Jeong, Yeonwoo, Lim, Jongkuk, Kim, Sujin
Format: Article
Language:English
Subjects:
Accelerometers
Activities of daily living
Asymmetry
Bone surgery
Classification
Deep learning
Evaluation
hemiparesis
human action recognition
Medical research
Medicine, Experimental
Neural networks
Orthopedics
Patients
range of motion
Sensors
Stroke
Stroke patients
stroke rehabilitation
Surveillance
Time series
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Summary:Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stroke rehabilitation. Classifying movements using non-intrusive wearable sensors provides context for arm use and is essential for the development of a home rehabilitation system. However, the movement classification of stroke patients poses unique challenges, including variability and sparsity. To address these challenges, we collected movement data from 15 hemiparetic stroke patients (Stroke group) and 29 non-disabled individuals (ND group). The participants performed two different tasks, the range of motion (14 movements) task and the activities of daily living (56 movements) task, wearing five inertial measurement units in a home setting. We trained a 1D convolutional neural network and evaluated its performance for different training groups: ND-only, Stroke-only, and ND and Stroke jointly. We further compared the model performance with data augmentation from axis rotation and investigated how the performance varied based on the asymmetry of movements. The joint training of ND + Stroke yielded an increased F1-score by a margin of 31.6% and 10.6% compared to ND-only training and Stroke-only training, respectively. Data augmentation further enhanced F1-scores across all conditions by an average of 11.3%. Finally, asymmetric movements decreased the F1-score by 25.9% compared to symmetric movements in the Stroke group, indicating the importance of asymmetry in movement classification.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24010210