Muscle synergy-informed neuromusculoskeletal modelling to estimate knee contact forces in children with cerebral palsy

Abstract

Cerebral palsy (CP) includes a group of neurological conditions caused by damage to the developing brain, resulting in maladaptive alterations of muscle coordination and movement. Estimates of joint moments and contact forces during locomotion are important to establish the trajectory of disease progression and plan appropriate surgical interventions in children with CP. Joint moments and contact forces can be estimated using electromyogram (EMG)-informed neuromusculoskeletal models, but a reduced number of EMG sensors would facilitate translation of these computational methods to clinics. This study developed and evaluated a muscle synergy-informed neuromusculoskeletal modelling approach using EMG recordings from three to four muscles to estimate joint moments and knee contact forces of children with CP and typically developing (TD) children during walking. Using only three to four experimental EMG sensors attached to a single leg and leveraging an EMG database of walking data of TD children, the synergy-informed approach estimated total knee contact forces comparable to those estimated by EMG-assisted approaches that used 13 EMG sensors (children with CP, n = 3, R2 = 0.95 ± 0.01, RMSE = 0.40 ± 0.14 BW; TD controls, n = 3, R2 = 0.93 ± 0.07, RMSE = 0.19 ± 0.05 BW). The proposed synergy-informed neuromusculoskeletal modelling approach could enable rapid evaluation of joint biomechanics in children with unimpaired and impaired motor control within a clinical environment.

Towards a comprehensive biomechanical assessment of the elderly combining in vivo data and in silico methods

“…To the authors’ knowledge, this is the first study showing such a comprehensive set of experimental measures and parameters, collected with the aim to fully characterize an individual from a biomechanical and neuromuscular standpoint. With a larger sample size at hand, one could apply data extraction and analytics approaches—eventually supported by machine learning or AI-based methods—to get insights into the mechanisms behind the loss of muscle force ( Giarmatzis et al, 2020 ; Yeung et al, 2020 ; Liew et al, 2023 ; Moghadam et al, 2023 ; Rabbi et al, 2024 ).…”