Ontogenetic changes in limb posture, kinematics, forces, and joint moments in American alligators (Alligator mississippiensis)

As animals increase in size, common patterns of morphological and physiological scaling may require them to perform behaviors such as locomotion while experiencing a reduced capacity to generate muscle force and an increased risk of tissue failure. Large mammals are known to manage increased mechanical demands by using more upright limb posture. However, the presence of such size-dependent changes in limb posture has rarely been tested in animals that use non-parasagittal limb kinematics. Here, we used juvenile to subadult American alligators (total length 0.46–1.27 m, body mass 0.3–5.6 kg) and examined their limb kinematics, forces, joint moments, and center of mass to test for ontogenetic shifts in posture and limb mechanics. Larger alligators typically walked with a more adducted humerus and femur and a more extended knee. Normalized peak joint moments reflected these postural patterns, with shoulder and hip moments imposed by the ground reaction force showing relatively greater magnitudes in the smallest individuals. Thus, as larger alligators use more upright posture, they incur relatively smaller joint moments than smaller alligators, which could reduce the forces that the shoulder and hip adductors of larger alligators must generate. The center of mass (CoM) shifted nonlinearly from juveniles through subadults. The more anteriorly positioned CoM in small alligators, together with their compliant hindlimbs, contributes to their higher forelimb and lower hindlimb normalized peak vertical forces in comparison to larger alligators. Future studies of alligators that approach maximal adult sizes could give further insight into how animals with non-parasagittal limb posture modulate locomotor patterns as they increase in mass and experience changes in the CoM.

insideOut: A Bio-Inspired Machine Learning Approach to Estimating Posture in Robots Driven by Compliant Tendons

Estimates of limb posture are critical for controlling robotic systems. This is generally accomplished with angle sensors at individual joints that simplify control but can complicate mechanical design and robustness. Limb posture should be derivable from each joint's actuator shaft angle but this is problematic for compliant tendon-driven systems where (i) motors are not placed at the joints and (ii) nonlinear tendon stiffness decouples the relationship between motor and joint angles. Here we propose a novel machine learning algorithm to accurately estimate joint posture during dynamic tasks by limited training of an artificial neural network (ANN) receiving motor angles and tendon tensions, analogous to biological muscle and tendon mechanoreceptors. Simulating an inverted pendulum—antagonistically-driven by motors and nonlinearly-elastic tendons—we compare how accurately ANNs estimate joint angles when trained with different sets of non-collocated sensory information generated via random motor-babbling. Cross-validating with new movements, we find that ANNs trained with motor angles and tendon tension data predict joint angles more accurately than ANNs trained without tendon tension. Furthermore, these results are robust to changes in network/mechanical hyper-parameters. We conclude that regardless of the tendon properties, actuator behavior, or movement demands, tendon tension information invariably improves joint angle estimates from non-collocated sensory signals.

Similar stretch reflexes and behavioral patterns are expressed by the dominant and non-dominant arms during postural control

Limb dominance is evident in many daily activities leading to the prominent idea that each hemisphere of the brain specializes in controlling different aspects of movement. Past studies suggest the dominant arm is primarily controlled via an internal model of limb dynamics that enables the nervous system to produce efficient movements. In contrast, the non-dominant arm may be primarily controlled via impedance mechanisms that rely on the strong modulation of sensory feedback from individual joints to control limb posture. We tested whether such differences are evident in behavioral responses and stretch reflexes following sudden displacement of the arm during posture control. Experiment 1 applied specific combinations of elbow-shoulder torque perturbations (the same for all participants). Peak joint displacements, return times, endpoint accuracy, and the directional tuning and amplitude of stretch reflexes in nearly all muscles were not statistically different between the two arms. Experiment 2 induced specific combinations of joint motion (the same for all participants). Again, peak joint displacements, return times, endpoint accuracy, and the directional tuning and amplitude of stretch reflexes in nearly all muscles did not differ statistically when countering the imposed loads with each arm. Moderate to strong correlations were found between stretch reflexes and behavioral responses to the perturbations with the two arms across both experiments. Collectively, the results do not support the idea that the dominant arm specializes in exploiting internal models and the non-dominant arm in impedance control by increasing reflex gains to counter sudden loads imposed on the arms during posture control.

The Effect of Sport Practice on Enhanced Cognitive Processing of Bodily Indices: A Study on Volleyball Players and Their Ability to Predict Hand Gestures

To program proper reactions, athletes must anticipate opponents’ actions on the basis of previous visuomotor experience. In particular, such abilities seem to rely on processing others’ intentions to act. We adopted a new approach based on an attentional spatial compatibility paradigm to investigate how elite volleyball players elaborate both spatial and motor information at upper-limb posture presentation. Forty-two participants (18 volleyball players and 17 nonathlete controls assigned to Experiments 1 a and b, and eight basketball players assigned to Experiment 2) were tested to study their ability to process the intentions to act conveyed by hands and extract motor primitives (i.e., significant components of body movements). Analysis looked for a spatial compatibility effect between direction of the spike action (correspondence factor) and response side for both palm and back of the hand (view factor). We demonstrated that volleyball players encoded spatial sport-related indices from bodily information and showed preparatory motor activation according to the direction of the implied spike actions for the palm view (Experiment 1; hand simulating a cross-court spike, p = 0.013, and a down-the-line spike, p = 0.026) but both nonathlete controls (Experiment 1; both p < 0.05) and other sports athletes (basketball players, Experiment 2; p = 0.34, only cross-court spike) did not. Results confirm that elite players’ supremacy lies in the predictive abilities of coding elementary motor primitives for their sport discipline.

A Control and Posture Recognition Strategy for Upper-Limb Rehabilitation of Stroke Patients

At present, the study of upper-limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper-limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine- (FSM-) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification.

Lower Limb Takeoff Mechanics During a Leap in Dancers With and Without Flexor Hallucis Longus Tendinopathy

OBJECTIVE: Altered ground reaction force (GRF) and joint torsional stiffness are associated with various lower extremity injuries, but these have yet to be examined in dancers with flexor hallucis longus (FHL) tendinopathy. Additionally, a simple, field-friendly kinematic correlate to ground contact kinetics would be useful for clinical application. The purpose of this study was to compare lower extremity biomechanics during takeoff of a dance leap (saut de chat) in dancers with and without FHL tendinopathy, and to examine lower limb posture at initial contact as a clinical correlate of injury-related kinetic factors. METHODS: Motion capture and inverse dynamics were used to analyze saut de chat takeoff performed by 11 uninjured dancers and 8 dancers with FHL tendinopathy. GRF parameters, joint torsional stiffness of the metatarsophalangeal, ankle, and knee joints, and lower extremity posture at initial contact were compared between groups using Welch’s t-tests. RESULTS: Dancers with FHL tendinopathy maintained similar jump height as the uninjured dancers, but exhibited lower peak vertical GRF, longer time to peak force, and less joint torsional stiffness at the metatarsophalangeal, ankle, and knee joints during loading response of the takeoff step. Lower extremity contact angle was smaller and the horizontal distance between center-of-mass and center-of-pressure was greater in dancers with FHL tendinopathy. These two measures of lower limb posture at initial contact were significantly correlated with kinetic factors occurring later in ground contact (R2=0.29-0.51). CONCLUSION: Dancers with FHL tendinopathy demonstrated altered lower extremity kinetics during takeoff of a leap compared to uninjured dancers, which may contribute to, or be a compensation response to, injury

Central processing of leg proprioception in Drosophila

Proprioception, the sense of self-movement and position, is mediated by mechanosensory neurons that detect diverse features of body kinematics. Although proprioceptive feedback is crucial for accurate motor control, little is known about how downstream circuits transform limb sensory information to guide motor output. Here we investigate neural circuits in Drosophila that process proprioceptive information from the fly leg. We identify three cell types from distinct developmental lineages that are positioned to receive input from proprioceptor subtypes encoding tibia position, movement, and vibration. 13Bα neurons encode femur-tibia joint angle and mediate postural changes in tibia position. 9Aα neurons also drive changes in leg posture, but encode a combination of directional movement, high frequency vibration, and joint angle. Activating 10Bα neurons, which encode tibia vibration at specific joint angles, elicits pausing in walking flies. Altogether, our results reveal that central circuits integrate information across proprioceptor subtypes to construct complex sensorimotor representations that mediate diverse behaviors, including reflexive control of limb posture and detection of leg vibration.