Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors

Conference paper


Samuel, O., Asogbon, M., Kulwa, F., Zangene, A., Oyemakinde, T., Igbe, T., McEwan, A., Li, Y. and Li, G. 2023. Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340683
AuthorsSamuel, O., Asogbon, M., Kulwa, F., Zangene, A., Oyemakinde, T., Igbe, T., McEwan, A., Li, Y. and Li, G.
TypeConference paper
Abstract

Motor function loss greatly impacts post-stroke survivors while performing activities of daily living. In the recent years, intelligent rehabilitation robotics have been proposed to enable the patients recover their lost limb functions. Besides, a large proportion of these robots function in passive mode that only allow users to navigate trajectories that rarely align with their limb movement intent, thus precluding full functional recovery. A potential solution would be to explore utilizing an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) to decode multiple classes of post-stroke patients’ motion intentions towards realizing dexterously active robotic training during rehabilitation. In this regard, we propose and examined for the first time, the use of Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns of stroke patients to provide adequate input for active motor training in rehabilitation robots. Importantly, we examined the proposed (STD-CWT) method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. Our method was validated using electromyogram signals of five stroke survivors who performed up to twenty-two distinct limb motions. The obtained results showed that the proposed technique recorded a significantly higher decoding (p<0.05) and converges faster compared to the commonly adopted method. The proposed method equally recorded obvious class separability for individual movement classes across the stroke patients. Findings from this study suggest that the STD-CWT Scalograms would provide potential inputs for robust decoding of motor intent that may facilitate intuitively active motor training in stroke rehabilitation robots.

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KeywordsRehabilitation Technology; AI and Machine Learning; Electromyogram; Pattern Recognition; Stroke Patients
Year2023
Conference45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
ISSN2694-0604
Digital Object Identifier (DOI)https://doi.org/10.1109/EMBC40787.2023.10340683
Web address (URL)https://www.embs.org/event/embc-embs-org-2023/
Accepted author manuscript
License
All rights reserved
File Access Level
Open
Journal citationpp. 1-5
ISBN9798350324471
Web address (URL) of conference proceedingshttps://embc.embs.org/2023/
Output statusPublished
Publication dates
Online11 Dec 2023
Publication process dates
AcceptedApr 2023
Deposited18 Dec 2023
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