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Rehab-Net: deep learning framework for arm movement classification using wearable sensors for stroke rehabilitation

Rehab-Net: deep learning framework for arm movement classification using wearable sensors for stroke rehabilitation
Rehab-Net: deep learning framework for arm movement classification using wearable sensors for stroke rehabilitation
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.
0018-9294
1-1
Panwar, Madhuri
4cb8a4b0-6315-47eb-99b5-1e0eaa173aaf
Biswas, Dwaipayan
314a210f-c293-4d18-8b07-ddaaf57a1707
Bajaj, Harsh
55297851-c158-4657-b440-89c2e4878e56
Jobges, Michael
56f78929-cd51-4837-930d-57efaee6d6ef
Turk, Ruth
985d334a-d337-48a6-a112-0665b82d78aa
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Acharyya, Amit
78ca46cd-5b42-48f9-91da-0ed05dff2023
Panwar, Madhuri
4cb8a4b0-6315-47eb-99b5-1e0eaa173aaf
Biswas, Dwaipayan
314a210f-c293-4d18-8b07-ddaaf57a1707
Bajaj, Harsh
55297851-c158-4657-b440-89c2e4878e56
Jobges, Michael
56f78929-cd51-4837-930d-57efaee6d6ef
Turk, Ruth
985d334a-d337-48a6-a112-0665b82d78aa
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Acharyya, Amit
78ca46cd-5b42-48f9-91da-0ed05dff2023

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, 1-1. (doi:10.1109/TBME.2019.2899927).

Record type: Article

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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e-pub ahead of print date: 18 February 2019

Identifiers

Local EPrints ID: 430951
URI: https://eprints.soton.ac.uk/id/eprint/430951
ISSN: 0018-9294
PURE UUID: bae5d2b3-c07b-4940-ba45-704ea2586706

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Date deposited: 17 May 2019 16:30
Last modified: 27 Aug 2019 16:30

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Contributors

Author: Madhuri Panwar
Author: Dwaipayan Biswas
Author: Harsh Bajaj
Author: Michael Jobges
Author: Ruth Turk
Author: Amit Acharyya

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