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A muscle synergy-based method to estimate muscle activation patterns of children with cerebral palsy using data
Mohammad Fazle Rabbi1, Laura E Diamond2, Chris P Carty2,3,4
1Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, and Advanced Design and Prototyping Technologies Institute, Griffith University, QLD, 4222, Southport, Australia. fazle.rabbi@griffithuni.edu.au.
Insights
This study developed a muscle synergy extrapolation method to estimate lower limb muscle activation in children with cerebral palsy using minimal electromyograms (EMG). This approach simplifies gait analysis preparation by reducing sensor needs.
Area of Science:
- Biomedical Engineering
- Rehabilitation Science
- Movement Science
Background:
- Gait analysis in children with cerebral palsy (CP) is time-intensive due to extensive sensor preparation.
- Minimizing electromyogram (EMG) sensors can improve clinical workflow efficiency.
- Accurate muscle activation patterns are crucial for effective clinical assessment in CP.
Purpose of the Study:
- To develop a method for estimating lower limb muscle activation patterns in children with CP from a limited number of EMG measurements.
- To reduce the complexity and time required for preparing children with CP for gait analysis.
Main Methods:
- Implemented a muscle synergy extrapolation method to estimate full muscle activation patterns.
- Extracted hybrid muscle synergies from EMG data of children with CP and healthy individuals.
- Validated the method's ability to predict activation patterns of unmeasured muscles.
Main Results:
- Two combinations of 3-4 EMG sensors accurately estimated 10 muscle activation patterns.
- The method achieved a mean variance accounted for of approximately 80% with only three muscle synergies.
- Successful estimation of muscle activation patterns for unmeasured muscles was demonstrated.
Conclusions:
- Muscle activation patterns in children with CP can be reliably estimated from 3-4 EMG measurements using the developed muscle synergy extrapolation method.
- This method has the potential to significantly reduce preparation time in clinical gait analysis settings.
- The muscle synergy-based approach offers a promising solution for streamlining assessments in pediatric rehabilitation.
Abstract:
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. Collecting minimum number of electromyograms (EMG) and yet providing adequate information for clinical assessment might improve clinical workflow. 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. 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. Specifically, we extracted a set of hybrid muscle synergies from muscle activation patterns of children with cerebral palsy and their healthy counterparts. Next, those muscle synergies were used to estimate activation patterns of muscles, which were not initially measured in children with cerebral palsy. 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. 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. In the future, the proposed muscle synergy-based method could be employed in gait clinics to minimise the required preparation time.
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