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IntroductionChildren start to run after they master walking. How running develops, however, is largely unknown.MethodsWe assessed the maturity of running pattern in two very young, typically developing children in a longitudinal design spanning about three years. Leg and trunk 3D kinematics and electromyography collected in six recording sessions, with more than a hundred strides each, entered our analysis. We recorded walking during the first session (the session of the first independent steps of the two toddlers at the age of 11.9 and 10.6 months) and fast walking or running for the subsequent sessions. More than 100 kinematic and neuromuscular parameters were determined for each session and stride. The equivalent data of five young adults served to define mature running. After dimensionality reduction using principal component analysis, hierarchical cluster analysis based on the average pairwise correlation distance to the adult running cluster served as a measure for maturity of the running pattern.ResultsBoth children developed running. Yet, in one of them the running pattern did not reach maturity whereas in the other it did. As expected, mature running appeared in later sessions (>13 months after the onset of independent walking). Interestingly, mature running alternated with episodes of immature running within sessions. Our clustering approach separated them.DiscussionAn additional analysis of the accompanying muscle synergies revealed that the participant who did not reach mature running had more differences in muscle contraction when compared to adults than the other. One may speculate that this difference in muscle activity may have caused the difference in running pattern.

Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the gold standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.

The first years of life might be critical for encouraging independent walking in children with cerebral palsy (CP). We sought to identify mechanisms that may underlie the impaired development of walking in three young children with early brain lesions, at high risk of CP, via comprehensive instrumented longitudinal assessments of locomotor patterns and muscle activation during walking. We followed three children (P1–P3) with early brain lesions, at high risk of CP, during five consecutive gait analysis sessions covering a period of 1 to 2 years, starting before the onset of independent walking, and including the session during the first independent steps. In the course of the study, P1 did not develop CP, P2 was diagnosed with unilateral and P3 with bilateral CP. We monitored the early development of locomotor patterns over time via spatiotemporal gait parameters, intersegmental coordination (estimated via principal component analysis), electromyography activity, and muscle synergies (determined from 11 bilateral muscles via nonnegative matrix factorization). P1 and P2 started to walk independently at the corrected age of 14 and 22 months, respectively. In both of them, spatiotemporal gait parameters, intersegmental coordination, muscle activation patterns, and muscle synergy structure changed from supported to independent walking, although to a lesser extent when unilateral CP was diagnosed (P2), especially for the most affected leg. The child with bilateral CP (P3) did not develop independent walking, and all the parameters did not change over time. Our exploratory longitudinal study revealed differences in maturation of locomotor patterns between children with divergent developmental trajectories. We succeeded in identifying mechanisms that may underlie impaired walking development in very young children at high risk of CP. When verified in larger sample sizes, our approach may be considered a means to improve prognosis and to pinpoint possible targets for early intervention.

Muscle synergies reflect the presence of a common neural input to multiple muscles. Steering small sets of synergies is commonly believed to simplify the control of complex motor tasks like walking and running. When these locomotor patterns emerge, it is likely that synergies emerge as well. We hence hypothesized that in children learning to run the number of accompanying synergies increases and that some of the synergies’ activities display a temporal shift related to a reduced stance phase as observed in adults. We investigated the development of locomotion in 23 children aged 2–9 years of age and compared them with seven young adults. Muscle activity of 15 bilateral leg, trunk, and arm muscles, ground reaction forces, and kinematics were recorded during comfortable treadmill walking and running, followed by a muscle synergy analysis. We found that toddlers (2–3.5 years) and preschoolers (3.5–6.5 years) utilize a “walk-run strategy” when learning to run: they managed the fastest speeds on the treadmill by combining double support (DS) and flight phases (FPs). In particular the activity duration of the medial gastrocnemius muscle was weakly correlated with age. The number of synergies across groups and conditions needed to cover sufficient data variation ranged between four and eight. The number of synergies tended to be smaller in toddlers than it did in preschoolers and school-age children but the adults had the lowest number for both conditions. Against our expectations, the age groups did not differ significantly in the timing or duration of synergies. We believe that the increase in the number of muscle synergies in older children relates to motor learning and exploration. The ability to run with a FP is clearly associated with an increase in the number of muscle synergies.

When walking speed is increased, the frequency ratio between the arm and leg swing switches spontaneously from 2:1 to 1:1. We examined whether these switches are accompanied by changes in functional connectivity between multiple muscles. Subjects walked on a treadmill with their arms swinging along their body while kinematics and surface electromyography (EMG) of 26 bilateral muscles across the body were recorded. Walking speed was varied from very slow to normal. We decomposed EMG envelopes and intermuscular coherence spectra using non-negative matrix factorization (NMF), and the resulting modes were combined into multiplex networks and analyzed for their community structure. We found five relevant muscle synergies that significantly differed in activation patterns between 1:1 and 2:1 arm-leg coordination and the transition period between them. The corresponding multiplex network contained a single module indicating pronounced muscle co-activation patterns across the whole body during a gait cycle. NMF of the coherence spectra distinguished three EMG frequency bands: 4–8, 8–22, and 22–60 Hz. The community structure of the multiplex network revealed four modules, which clustered functional and anatomical linked muscles across modes of coordination. Intermuscular coherence at 4–22 Hz between upper and lower body and within the legs was particularly pronounced for 1:1 arm-leg coordination and was diminished when switching between modes of coordination. These findings suggest that the stability of arm-leg coordination is associated with modulations in long-distant neuromuscular connectivity.

Background: Walking problems in children with cerebral palsy (CP) can in part be explained by limited selective motor control. Muscle synergy analysis is increasingly used to quantify altered neuromuscular control during walking. The early brain injury in children with CP may lead to a different development of muscle synergies compared to typically developing (TD) children, which might characterize the abnormal walking patterns.Objective: The overarching aim of this review is to give an overview of the existing studies investigating muscle synergies during walking in children with CP compared to TD children. The main focus is on how muscle synergies differ between children with CP and TD children, and we examine the potential of muscle synergies as a measure to quantify and predict treatment outcomes.Methods: Bibliographic databases were searched by two independent reviewers up to 22 April 2019. Studies were included if the focus was on muscle synergies of the lower limbs during walking, obtained by a matrix factorization algorithm, in children with CP.Results: The majority (n = 12) of the 16 included studies found that children with CP recruited fewer muscle synergies during walking compared to TD children, and several studies (n = 8) showed that either the spatial or temporal structure of the muscle synergies differed between children with CP and TD children. Variability within and between subjects was larger in children with CP than in TD children, especially in more involved children. Muscle synergy characteristics before treatments to improve walking function could predict treatment outcomes (n = 3). Only minimal changes in synergies were found after treatment.Conclusions: The findings in this systematic review support the idea that children with CP use a simpler motor control strategy compared to TD children. The use of muscle synergy analysis as a clinical tool to quantify altered neuromuscular control and predict clinical outcomes seems promising. Further investigation on this topic is necessary, and the use of muscle synergies as a target for development of novel therapies in children with CP could be explored.

BackgroundChildren with cerebral palsy (CP) often show impaired selective motor control (SMC) that induces limitations in motor function. Children with CP can improve aspects of pathological gait in an immediate response to visual biofeedback. It is not known, however, how these gait adaptations are achieved at the neural level, nor do we know the extent of SMC plasticity in CP.AimInvestigate the underlying SMC and changes that may occur when gait is adapted with biofeedback.MethodsTwenty-three ambulatory children with CP and related (hereditary) forms of spastic paresis (Aged: 10.4 ± 3.1, 6–16 years, M: 16/F: 9) were challenged with real-time biofeedback to improve step length, knee extension, and ankle power while walking on an instrumented treadmill in a virtual reality environment. The electromyograms of eight superficial muscles of the leg were analyzed and synergies were further decomposed using non-negative matrix factorization (NNMF) using 1 to 5 synergies, to quantify SMC. Total variance accounted for (tVAF) was used as a measure of synergy complexity. An imposed four synergy solution was investigated further to compare similarity in weightings and timing patterns of matched paired synergies between baseline and biofeedback trials.ResultsDespite changes in walking pattern, changes in synergies were limited. The number of synergies required to explain at least 90% of muscle activation increased significantly, however, the change in measures of tVAF1 from baseline (0.75 ± 0.08) were less than ±2% between trials. In addition, within-subject similarity of synergies to baseline walking was high (>0.8) across all biofeedback trials.ConclusionThese results suggest that while gait may be adapted in an immediate response, SMC as quantified by synergy analysis is perhaps more rigidly impaired in CP. Subtle changes in synergies were identified; however, it is questionable if these are clinically meaningful at the level of an individual. Adaptations may be limited in the short term, and further investigation is essential to establish if long term training using biofeedback leads to adapted SMC.