Panwar, Madhuri, Biswas, Dwaipayan, Bajaj, Harsh, Jobges, Michael, Turk, Ruth, Maharatna, Koushik and Acharyya, Amit (2019) Rehab-Net: deep learning framework for arm movement classification using wearable sensors for stroke rehabilitation. IEEE Transactions on Biomedical Engineering, 66 (1), 3026-3037. (doi:10.1109/TBME.2019.2899927).
Abstract
In this paper, we present a deep learning framework ‘Rehab-Net’ for effectively classifying three upper limb movements of the human arm, involving extension, flexion and rotation of the forearm which over the time could provide a measure of rehabilitation progress. The proposed framework, Rehab-Net is formulated with a personalized, light weight and low complex, customized CNN model, using 2-layers of Convolutional neural network (CNN), interleaved with pooling layers, followed by a fully-connected layer that classifies the three movements from tri-axial acceleration input data collected from the wrist. The proposed Rehab-net framework was validated on sensor data collected in two situations-a) seminaturalistic environment involving an archetypal activity of ‘making-tea’ with 4 stroke survivors and b) natural environment, where 10 stroke survivors were free to perform any desired arm movement for a duration of 120 minutes. We achieve an overall accuracy of 97.89% on semi-naturalistic data and 88.87% on naturalistic data which exceeded state-of-the-art learning algorithms namely, Linear Discriminant Analysis, Support Vector Machines, and k-means clustering with an average accuracy of 48.89%, 44.14% and 27.64%. Subsequently, a computational complexity analysis of the proposed model has been discussed with an eye towards hardware implementation. The clinical significance of this study is to accurately monitor the clinical progress of the rehabilitated subjects under the ambulatory settings.
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- Faculties (pre 2011 reorg) > Faculty of Engineering Science & Maths (pre 2011 reorg) > Electronics & Computer Science (pre 2011 reorg)
Current Faculties > Faculty of Engineering and Physical Sciences > School of Electronics and Computer Science > Electronics & Computer Science (pre 2011 reorg)
School of Electronics and Computer Science > Electronics & Computer Science (pre 2011 reorg) - Faculties (pre 2018 reorg) > Faculty of Natural and Environmental Sciences (pre 2018 reorg) > Institute for Life Sciences (pre 2018 reorg)
Current Faculties > Faculty of Environmental and Life Sciences > Institute for Life Sciences > Institute for Life Sciences (pre 2018 reorg)
Institute for Life Sciences > Institute for Life Sciences (pre 2018 reorg) - Faculties (pre 2018 reorg) > Faculty of Physical Sciences and Engineering (pre 2018 reorg) > Electronics & Computer Science (pre 2018 reorg)
Current Faculties > Faculty of Engineering and Physical Sciences > School of Electronics and Computer Science > Electronics & Computer Science (pre 2018 reorg)
School of Electronics and Computer Science > Electronics & Computer Science (pre 2018 reorg) - Current Faculties > Faculty of Environmental and Life Sciences > School of Health Sciences > Allied Health Professions > Physiotherapy
School of Health Sciences > Allied Health Professions > Physiotherapy - Current Faculties > Faculty of Engineering and Physical Sciences > School of Electronics and Computer Science > Biomedical Electronics
School of Electronics and Computer Science > Biomedical Electronics
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