Signal Processing for Spinal Cord Injury Monitoring with sEMG
Nishit Agarwal, Shreyas Mahimkar, S. Purna Chandra Shekhar, Prof. Arpit Jain, Prof. Punit Goel
Abstract
Spinal cord injuries (SCIs) significantly impair motor and sensory functions, posing challenges to rehabilitation and patient management. Accurate monitoring of SCIs is crucial for evaluating the extent of injury and guiding therapeutic interventions. This study explores the application of surface electromyography (sEMG) as a non-invasive tool for monitoring muscle activity in individuals with SCIs. By capturing electrical signals generated by muscle fibers, sEMG provides real-time data essential for assessing neuromuscular function. To enhance the effectiveness of sEMG in spinal cord injury monitoring, advanced signal processing techniques are employed. These techniques include noise reduction, which improves signal clarity, feature extraction to identify key patterns in muscle activation, and pattern recognition algorithms to classify muscle responses. The integration of these methods enables more accurate interpretations of sEMG data, facilitating a better understanding of motor capabilities and rehabilitation progress. The findings highlight the potential of using signal processing in conjunction with sEMG to improve patient outcomes, optimize rehabilitation protocols, and provide insights into the underlying mechanisms of spinal cord injuries. This approach not only advances clinical practices but also supports ongoing research aimed at enhancing the quality of life for individuals affected by SCIs.