Development of vertical and forward jumping skills in typically developing children in the context of referent control of motor actions

Agreement between Force Platform and Smartphone Application-Derived Measures of Vertical Jump Height in Youth Grassroots Soccer Players

Given the importance of vertical jump assessments as a performance benchmarking tool, the assessment of neuromuscular function and indicator of health status, accurate assessment is essential. This study compared countermovement jump (CMJ) height assessed using MyJump2 (JH_MJ) to force-platform-derived jump height calculated from time in the air (JH_TIA) and take-off velocity (JH_TOV) in youth grassroots soccer players. Thirty participants (Age: 8.7 ± 0.42 yrs; 9 females) completed bilateral CMJs on force platforms whilst jump height was simultaneously evaluated using MyJump2. Intraclass correlation coefficients (ICC), Standard error of measurement (SEM), coefficient of variance (CV) and Bland-Altman analysis were used to compare performance of MyJump2 to force-platform-derived measures of CMJ height. The median jump height was 15.5 cm. Despite a high level of agreement between JH_TIA and JH_TOV (ICC = 0.955), CV (6.6%), mean bias (1.33 ± 1.62 cm) and 95% limits of agreement (LoA -1.85-4.51 cm) were greater than in other comparisons. JH_MJ performed marginally better than JH_TIA when compared to JH_TOV (ICC = 0.971; 95% CI's = 0.956-0.981; SEM = 0.3 cm; CV = 5.7%; mean bias = 0.36 ± 1.61 cm; LoA = -3.52-2.80 cm). Irrespective of method, jump height did not differ between males and females (p > 0.381; r < 0.093), and the comparison between assessment tools was not affected by sex. Given low jump heights achieved in youth, JH_TIA and JH_MJ should be used with caution. JH_TOV should be used to guarantee accuracy in the calculation of jump height.

Reaction of human walking to transient block of vision: analysis in the context of indirect, referent control of motor actions

Human locomotion may result from monotonic shifts in the referent position, R, of the body in the environment. R is also the spatial threshold at which muscles can be quiescent but are activated depending on the deflection of the current body configuration Q from R. Shifts in R are presumably accomplished with the participation of proprioceptive and visual feedback and responsible for transferring stable body balance (equilibrium) from one place in the environment to another, resulting in rhythmic activity of multiple muscles by a central pattern generator (CPG). We tested predictions of this two-level control scheme. In particular, in response to a transient block of vision during locomotion, the system can temporarily slow shifts in R. As a result, the phase of rhythmical movements of all four limbs will be changed for some time, even though the rhythm and other characteristics of locomotion will be fully restored after perturbation, a phenomenon called long-lasting phase resetting. Another prediction of the control scheme is that the activity of multiple muscles of each leg can be minimized reciprocally at specific phases of the gait cycle both in the presence and absence of vision. Speed of locomotion is related to the rate of shifts in the referent body position in the environment. Results confirmed that human locomotion is likely guided by feedforward shifts in the referent body location, with subsequent changes in the activity of multiple muscles by the CPG. Neural structures responsible for shifts in the referent body configuration causing locomotion are suggested.

Do Primary School Children Benefit from Drop-Jump Training with Different Schedules of Augmented Feedback about the Jump Height?

In children, the training of jumps leads to improved jumping and running performance. Augmented feedback about the jump height is known to facilitate performance improvements in adults. In the present study, the impact of augmented feedback on jumping performance was investigated in 4th grade primary school children executing drop-jump training for 8 weeks (24 sessions, 3 times/week). Ten children (eight males, two females, aged 9.6 ± 0.3 years), received feedback for 8 weeks, and 11 children (nine males, two females, aged 9.5 ± 0.2 years) received feedback only during the last 4 weeks. Drop-jumps training was integrated in physical education classes. Drop-jump and countermovement-jump heights were improved after 24 training sessions (p < 0.01 for both types of jumps in both groups). Ground contact times of drop-jumps were quite long (>200 ms) and not altered by training, and the reactive strength index of drop-jumps was between 0.75 and 1.5 in most children. Augmented feedback did not facilitate jumping performance like in previous studies with adult participants. In contrast, withholding augmented feedback during the first 4 weeks of training was associated with a reduction in jumping performance (p < 0.01 for drop-jumps, p < 0.05 for countermovement-jumps). Finally, improvements did not transfer to functional motor tasks containing jumps. According to the costs and outcomes we do not recommend drop-jump training with augmented feedback about the jump height for 4th grade physical education classes.

Motor Control: A Conceptual Framework for Rehabilitation

There is a lack of conceptual and theoretical clarity among clinicians and researchers regarding the control of motor actions based on the use of the term "motor control." It is important to differentiate control processes from observations of motor output to improve communication and to make progress in understanding motor disorders and their remediation. This article clarifies terminology related to theoretical concepts underlying the control of motor actions, emphasizing how the term "motor control" is applied in neurorehabilitation. Two major opposing theoretical frameworks are described (i.e., direct and indirect), and their strengths and pitfalls are discussed. Then, based on the proposition that sensorimotor rehabilitation should be predicated on one comprehensive theory instead of an eclectic mix of theories and models, several solutions are offered about how to address controversies in motor learning, optimality, and adaptability of movement.

Central pattern generator and human locomotion in the context of referent control of motor actions

Unperturbed human locomotion presumably results from feedforward shifts in stable body equilibrium in the environment, thus avoiding falling and subsequent catching considered in alternative theories of locomotion. Such shifts are achieved by relocation of the referent body configuration at which multiple muscle recruitment begins. Rather than being directly specified by a central pattern generator, multiple muscles are activated depending on the extent to which the body is deflected from the referent, threshold body configuration, as confirmed in previous studies. Based on the referent control theory of action and perception, solutions to classical problems in motor control are offered, including the previously unresolved problem of the integration of central and reflex influences on motoneurons and the problem of how posture and movement are related. The speed of locomotion depends on the rate of shifts in the referent body configuration. The transition from walking to running results from increasing the rate of referent shifts. It is emphasised that there is a certain hierarchy between reciprocal and co-activation of agonist and antagonist muscles during locomotion and other motor actions, which is also essential for the understanding of how locomotor speed is regulated. The analysis opens a new avenue in neurophysiological approaches to human locomotion with clinical implications.

Research on Generation Method of Grasp Strategy Based on DeepLab V3+ for Three-Finger Gripper

A reasonable grasping strategy is a prerequisite for the successful grasping of a target, and it is also a basic condition for the wide application of robots. Presently, mainstream grippers on the market are divided into two-finger grippers and three-finger grippers. According to human grasping experience, the stability of three-finger grippers is much better than that of two-finger grippers. Therefore, this paper’s focus is on the three-finger grasping strategy generation method based on the DeepLab V3+ algorithm. DeepLab V3+ uses the atrous convolution kernel and the atrous spatial pyramid pooling (ASPP) architecture based on atrous convolution. The atrous convolution kernel can adjust the field-of-view of the filter layer by changing the convolution rate. In addition, ASPP can effectively capture multi-scale information, based on the parallel connection of multiple convolution rates of atrous convolutional layers, so that the model performs better on multi-scale objects. The article innovatively uses the DeepLab V3+ algorithm to generate the grasp strategy of a target and optimizes the atrous convolution parameter values of ASPP. This study used the Cornell Grasp dataset to train and verify the model. At the same time, a smaller and more complex dataset of 60 was produced according to the actual situation. Upon testing, good experimental results were obtained.

Physiological Profile Assessment of Posture in Children and Adolescents with Autism Spectrum Disorder and Typically Developing Peers

A sound postural system requires sensorimotor integration. Evidence suggests that individuals with Autism Spectrum Disorder (ASD) present sensorimotor integration impairments. The Physiological Profile Assessment (PPA) can be used to evaluate postural capacity assessing five physiological subsets (i.e., vision, reaction time, peripheral sensation, lower limb strength, balance); however, no studies applied the PPA in young individuals. Therefore, this study aimed to investigate the PPA in children and adolescents with ASD compared with age-matched typically developing (TD) individuals and examine the relationship between the PPA subset within the ASD and TD participants according to different age groups. Percentiles from the PPA were obtained from the TD children and adolescents (n = 135) for each test. Performances of the individuals with ASD (n = 18) were examined relative to the TD percentiles. ASD participants' scores were above the 90th percentile (i.e., poor performance) in most sensory, motor and balance parameters. Performance in most of the PPA tests significantly improved with older age in the TD group but not in the ASD group. The study findings support the use of the PPA in TD children and adolescents while further research should investigate postural capacity in a larger ASD sample to enhance the understanding of sensorimotor systems contributing to compromised postural control.