Musculoskeletal Model to Predict Muscle Activity During Upper Limb Movement

Assessing biomechanics of upper limb movement is essential for guiding targeted therapy to treat conditions such as spasticity and dystonia. Targeted therapy, including injections of medications into specific muscles (e.g., lidocaine, botulinum toxin type A), requires accurate identification (activity) and contribution of as many muscles as possible. Currently, this is achieved by visual clinical assessment or using surface electromyography (sEMG). Although sEMG could provide a reasonable estimate of muscle activity for certain superficial muscles after an intense filtering process, they are unable to provide separated activity and contribution for every superficial and deep muscle. Other proposed musculoskeletal and machine learning models similarly do not provide a detailed and accurate activity of every muscle. The objective of the study is to design a subject-specific musculoskeletal model to predict the activity and contribution of each muscle pertaining to any upper limb movement with improved detail and accuracy over existing methodologies. Performance metrics were calculated for validation by comparing the predicted muscle activity with the normalized sEMG data computed from 8 superficial muscles, while the deeper muscles were not included in the validation as the sEMG is unable to provide a separated activity for those muscles. The results show that the proposed model has a mean R
²
value of 0.8190 and also indicated a statistically significant correlation ( P <; 0.0001) between the calculated (normalized sEMG data) and predicted activity value. Additionally, and significantly, compared to earlier studies, the proposed model predicts the individual muscle activity and contribution of deeper muscles.