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

• Published in: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2023; pp. 1 - 4
• Main Authors: Samuel, Oluwarotimi Williams, Asogbon, Mojisola G., Kulwa, Frank, Zangene, Alireza Rezaie, Oyemakinde, Tolulope T., Igbe, Tobore, McEwan, Alistair A., Li, Yongcheng, Li, Guanglin
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2023
• Subjects: Assistive robots; Continuous wavelet transforms; Decoding; Electromyography; Humans; Intention; Machine Learning; Stroke (medical condition); Stroke - diagnosis; Survivors; Training; Transfer learning; Upper Extremity
• ISSN: 2694-0604
• DOI: 10.1109/EMBC40787.2023.10340683

Intelligent rehabilitation robotics (RR) have been proposed in recent years to aid post-stroke survivors recover their lost limb functions. However, a large proportion of these robotic systems operate in a passive mode that restricts users to predefined trajectories that rarely align with their intended limb movements, precluding full functional recovery. To address this issue, an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) model is proposed to decode post-stroke patients' motion intentions toward realizing dexterously active robotic training during rehabilitation. For the first time, we use Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns. We evaluated the 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. We then validated the method using electromyogram signals of five stroke survivors who performed twenty-one distinct motor tasks. The results showed that the proposed technique recorded a significantly higher (p<0.05) decoding accuracy and faster convergence compared to the common method. Our method equally recorded obvious class separability for individual motor tasks across subjects. The findings suggest that the STD-CWT Scalograms have the potential for robust decoding of motor intention and could facilitate intuitive and active motor training in stroke RR.Clinical Relevance- The study demonstrated the potential of Spatial Temporal based Scalograms in aiding precise and robust decoding of multi-class motor tasks, upon which dexterously active rehabilitation robotic training for full motor function restoration could be realized.