A muscle synergy-based method to estimate muscle activation patterns of children with cerebral palsy using data collected from typically developing children

Rabbi, Mohammad Fazle Alam; Diamond, Laura E.; Carty, Christopher P.; Lloyd, David G.; Davico, Giorgio; Pizzolato, Claudio

doi:10.1038/s41598-022-07541-5

Cited by 16 publications

(17 citation statements)

References 38 publications

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“…Regarding the employed algorithms so far, the screened studies are uniform. Non-negative matrix factorization is considered the most appropriate method for extraction of muscle synergies in walking and running ( Rabbi et al, 2022 ) and all the studies (except one) employed it (or its variation WNMF) to extract synergies. This can be now considered as a consolidated standard, allowing fair comparison of the results between studies.…”

Section: Discussionmentioning

confidence: 99%

“…An interesting approach to simplify synergy extraction protocols and to ease the adoption of synergies has been offered by Rabbi et al who have proposed a new model of extracting muscle synergies based on limiting to the minimum the number of the analyzed muscles in CP children and to reconstruct the EMG signal of the missing muscles from what is observed in TD children, thus increasing the clinical applicability of the employed methods. This approach reduces the time needed for data acquisition and therefore patients’ stress, making muscle synergy analysis more applicable also in clinical practice ( Rabbi et al, 2022 ). Besides the need of analyzing more subjects to validate the previously mentioned method, we believe that reducing the number of probes is a promising approach, even if there are some muscles that must be always recorded in CP patients, being the ones forming the CP-specific synergies, as the tibialis anterior, rectus femoris, medial hamstrings and gastrocnemii.…”

Section: Discussionmentioning

confidence: 99%

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Recent developments in muscle synergy analysis in young people with neurodevelopmental diseases: A Systematic Review

Beltrame

Scano

Marino

et al. 2023

Front. Bioeng. Biotechnol.

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The central nervous system simplifies motor control by sending motor commands activating groups of muscles, known as synergies. Physiological locomotion can be described as a coordinated recruitment of four to five muscle synergies. The first studies on muscle synergies in patients affected by neurological diseases were on stroke survivors. They showed that synergies can be used as biomarkers for motor impairment as they vary in patients with respect to healthy people. Likewise, muscle synergy analysis has been applied to developmental diseases (DD). The need for a comprehensive view of the present findings is crucial for comparing results achieved so far and promote future directions in the field. In the present review, we screened three scientific databases and selected thirty-six papers investigating muscle synergies extracted from locomotion in children affected by DD. Thirty-one articles investigate how cerebral palsy (CP) influences motor control, the currently exploited method in studying motor control in CP and finally the effects of treatments in these patients in terms of synergies and biomechanics; two articles investigate how muscle synergies vary in Duchenne muscular dystrophy (DMD), and three other articles assess other developmental pathologies, such as chronic and acute neuropathic pain. For CP, most of the studies demonstrate that the number of synergies is lower and that the synergy composition varies in the affected children with respect to normal controls. Still, the predictability of treatment’s effects and the etiology of muscle synergy variation are open questions, as it has been reported that treatments minimally modify synergies, even if they improve biomechanics. The application of different algorithms in extracting synergies might bring about more subtle differences. Considering DMD, no correlation was found between non-neural muscle weakness and muscle modules’ variation, while in chronic pain a decreased number of synergies was observed as a possible consequence of plastic adaptations. Even if the potential of the synergistic approach for clinical and rehabilitation practices is recognized, there is not full consensus on protocols nor widely accepted guidelines for the systematic clinical adoption of the method in DD. We critically commented on the current findings, on the methodological issues and the relative open points, and on the clinical impact of muscle synergies in neurodevelopmental diseases to fill the gap for applying the method in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Recent developments in muscle synergy analysis in young people with neurodevelopmental diseases: A Systematic Review

Beltrame

Scano

Marino

et al. 2023

Front. Bioeng. Biotechnol.

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“…The combinations of ratio-based body measurements in the walk patterns obtained with the above-described datasets were established according to the combination rule in Equation (6), and no combinations were repeated for different orders of ratio-based body measurements. This process of creating a combination of features have been used by the current studies [ 35 , 36 ].

…”

Section: Methodsmentioning

confidence: 99%

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method

et al. 2022

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Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

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“…The evidence of simplified control strategies employed by individuals with CP led us to speculate that synergies from children with CP could be considered a subset of those used by typically developing (TD) children. This speculation was supported by the ability to reconstruct a full set of lower limb muscle excitations for children with CP combining minimal experimental EMG data and an existing database of muscle excitations from TD children (Rabbi et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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

Rabbi,

Davico,

Lloyd

et al. 2024

Biomech Model Mechanobiol

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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.

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A muscle synergy-based method to estimate muscle activation patterns of children with cerebral palsy using data collected from typically developing children

Cited by 16 publications

References 38 publications

Recent developments in muscle synergy analysis in young people with neurodevelopmental diseases: A Systematic Review

Recent developments in muscle synergy analysis in young people with neurodevelopmental diseases: A Systematic Review

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method

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

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