These methods assume recorded muscle activation patterns to be linear combinations of excitation primitives, which represent the temporal profile of muscle synergies 10, 11. Muscle synergies are extracted from processed electromyograms (EMG), known as muscle activation patterns, using mathematical factorisation methods 9. Muscle synergies reflect motor control arising from the interaction between descending supraspinal motor commands and sensory inflow from peripheral mechanoreceptors 2, and can be used to gauge motor impairments in people with neurological conditions 3, 4, including stroke survivors 5, children with cerebral palsy (CP) 6, and individuals with spinal cord injury 7 and Parkinson’s disease 8. The coordinated activation of multiple muscles during rhythmic movements, such as walking, is commonly referred to as a muscle synergy 1. In the future, the proposed muscle synergy-based method could be employed in gait clinics to minimise the required preparation time. In conclusion, muscle activation patterns of unmeasured muscles in children with cerebral palsy can be estimated from EMG measured from three to four muscles using our muscle synergy extrapolation method. Two best combinations with three (medial gastrocnemius, semi membranous, and vastus lateralis) and four (lateral gastrocnemius, semi membranous, sartorius, and vastus medialis) experimental EMG were able to estimate the full set of 10 muscle activation patterns with mean (± standard deviation) variance accounted for of 79.93 (± 9.64)% and 79.15 (± 6.40)%, respectively, using only three muscle synergies. Next, those muscle synergies were used to estimate activation patterns of muscles, which were not initially measured in children with cerebral palsy. Specifically, we extracted a set of hybrid muscle synergies from muscle activation patterns of children with cerebral palsy and their healthy counterparts. We developed and implemented a muscle synergy extrapolation method able to estimate the full set of lower limbs muscle activation patterns from only three experimentally measured EMG. The main goal of this study was to develop a method to estimate activation patterns of lower limb muscles from EMG measured from a small set of muscles in children with cerebral palsy. Collecting minimum number of electromyograms (EMG) and yet providing adequate information for clinical assessment might improve clinical workflow. Preparing children with cerebral palsy prior to gait analysis may be a challenging and time-intensive task, especially when large number of sensors are involved.
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