Morufu Olusola Ibitoye1, Nur Azah Hamzaid2, Ahmad Khairi Abdul Wahab3, Nazirah Hasnan4, Sunday Olusanya Olatunji5, Glen M Davis6. 1. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia; Department of Biomedical Engineering, Faculty of Engineering and Technology, University of Ilorin, P.M.B 1515, Ilorin, Nigeria. Electronic address: ibitoye.mo@unilorin.edu.ng. 2. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia. Electronic address: azah.hamzaid@um.edu.my. 3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia. 4. Department of Rehabilitation Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, 50603, Malaysia. 5. Computer Science Department, College of Computer Science & Information Technology, University of Dammam, Dammam, 34212, Saudi Arabia. 6. Clinical Exercise and Rehabilitation Unit, Discipline of Exercise and Sports Sciences, Faculty of Health Sciences, The University of Sydney, Sydney, NSW, Australia; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia.
Abstract
BACKGROUND AND OBJECTIVE: Using traditional regression modelling, we have previously demonstrated a positive and strong relationship between paralyzed knee extensors' mechanomyographic (MMG) signals and neuromuscular electrical stimulation (NMES)-assisted knee torque in persons with spinal cord injuries. In the present study, a method of estimating NMES-evoked knee torque from the knee extensors' MMG signals using support vector regression (SVR) modelling is introduced and performed in eight persons with chronic and motor complete spinal lesions. METHODS: The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension. RESULTS: When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R2), with a Gaussian support vector kernel, the R2 value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively. CONCLUSIONS: These results showed good predictive accuracy for SVR modelling, which can be generalized, and suggested that the MMG signals from paralyzed knee extensors are a suitable proxy for the NMES-assisted torque produced during repeated bouts of isometric knee extension tasks. This finding has potential implications for using MMG signals as torque sensors in NMES closed-loop systems and provides valuable information for implementing this method in research and clinical settings.
BACKGROUND AND OBJECTIVE: Using traditional regression modelling, we have previously demonstrated a positive and strong relationship between paralyzed knee extensors' mechanomyographic (MMG) signals and neuromuscular electrical stimulation (NMES)-assisted knee torque in persons with spinal cord injuries. In the present study, a method of estimating NMES-evoked knee torque from the knee extensors' MMG signals using support vector regression (SVR) modelling is introduced and performed in eight persons with chronic and motor complete spinal lesions. METHODS: The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension. RESULTS: When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R2), with a Gaussian support vector kernel, the R2 value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively. CONCLUSIONS: These results showed good predictive accuracy for SVR modelling, which can be generalized, and suggested that the MMG signals from paralyzed knee extensors are a suitable proxy for the NMES-assisted torque produced during repeated bouts of isometric knee extension tasks. This finding has potential implications for using MMG signals as torque sensors in NMES closed-loop systems and provides valuable information for implementing this method in research and clinical settings.