Posture-movement responses to stance perturbations and upper limb fatigue during a repetitive pointing task

The order of stimuli matters when learning second-order transitional probabilities

The order of stimuli within sequences and the transitional probabilities (TPs) it generates are central information in sequence processing. However, less is known about what type of information and how it is extracted by general learning mechanisms. The present study focused on statistical learning of second-order TPs. Second-order TPs are involved when only the combination of two stimuli predicts the third. In a first experiment, TPs depended crucially on the order of presentation of a pair A - B , which led to different predictions depending on the order of the stimuli (i.e., ABC vs. BAF). Eight visuomotor sequences governed by second-order TPs were used and response times (RTs) were recorded for each transition. The task included a learning phase followed by a switch phase during which the second-order TP were reversed (e.g., the sequences ABC and BAF became respectively ABF and BAC). A decrease of RTs between the second and the third stimulus during the learning phase and an increase of RTs during the switch phase suggested that variations of orders within second-order TPs could be learned. Further analyses, however, indicated that such learning was difficult for most participants. A second experiment showed that the difficulty of learning was not solely due to the difficulty to pick up the effect of order of presentation, but that learning second-order transitional probabilities in addition to order would be the main obstacle. These experiments suggest that statistical learning is capable of learning complex associations, even if this remains a challenge for human cognition.

Force reserve predicts compensation in reaching movement with induced shoulder strength deficit

Following events such as fatigue or stroke, individuals often move their trunks forward during reaching, leveraging a broader muscle group even when only arm movement would suffice. In previous work, we showed the existence of a "force reserve": a phenomenon where individuals, when challenged with a heavy weight, adjusted their motor coordination to preserve approximately 40% of their shoulder's force. Here, we investigated if such reserve can predict hip, shoulder, and elbow movements and torques resulting from an induced shoulder strength deficit. We engaged 20 healthy participants in a reaching task with incrementally heavier dumbbells, analyzing arm and trunk movements via motion capture and joint torques through inverse dynamics. We simulated these movements using an optimal control model of a 3-degree-of-freedom upper body, contrasting three cost functions: traditional sum of squared torques, a force reserve function incorporating a nonlinear penalty, and a normalized torque function. Our results demonstrate a clear increase in trunk movement correlated with heavier dumbbell weights, with participants employing compensatory movements to maintain a shoulder force reserve of approximately 40% of maximum torque. Simulations showed that while traditional and reserve functions accurately predicted trunk compensation, only the reserve function effectively predicted joint torques under heavier weights. These findings suggest that compensatory movements are strategically employed to minimize shoulder effort and distribute load across multiple joints in response to weakness. We discuss the implications of the force reserve cost function in the context of optimal control of human movements and its relevance for understanding compensatory movements poststroke.NEW & NOTEWORTHY Our study reveals key findings on compensatory movements during upper limb reaching tasks under shoulder strength deficits, as observed poststroke. Using heavy dumbbells with healthy volunteers, we demonstrate how forward trunk displacement conserves around 40% of shoulder strength reserve during reaching. We show that an optimal controller employing a cost function combining squared motor torque and a nonlinear penalty for excessive muscle activation outperforms traditional controllers in predicting torques and compensatory movements in these scenarios.

The reserve of joint torque determines movement coordination

Humans coordinate biomechanical degrees of freedom to perform tasks at minimum cost. When reaching a target from a seated position, the trunk-arm-forearm coordination moves the hand to the well-defined spatial goal, while typically minimising hand jerk and trunk motion. However, due to fatigue or stroke, people visibly move the trunk more, and it is unclear what cost can account for this. Here we show that people recruit their trunk when the torque at the shoulder is too close to the maximum. We asked 26 healthy participants to reach a target while seated and we found that the trunk contribution to hand displacement increases from 11 to 27% when an additional load is handled. By flexing and rotating the trunk, participants spontaneously increase the reserve of anti-gravitational torque at the shoulder from 25 to 40% of maximal voluntary torque. Our findings provide hints on how to include the reserve of torque in the cost function of optimal control models of human coordination in healthy fatigued persons or in stroke victims.

Fatigue, induced via repetitive upper-limb motor tasks, influences trunk and shoulder kinematics during an upper limb reaching task in a virtual reality environment

BACKGROUND

Efficient shoulder movement depends on the ability of central nervous system to integrate sensory information and to create an appropriate motor command. Various daily encountered factors can potentially compromise the execution of the command, such as fatigue. This study explored how fatigue influences shoulder movements during upper limb reaching.

METHODS

Forty healthy participants were randomly assigned to one of two groups: Control or Fatigue Group. All participants completed an upper limb reaching task at baseline and post-experimental, during which they reached four targets located at 90° of shoulder abduction, 90° external rotation at 90° abduction, 120° scaption, and 120° flexion in a virtual reality environment. Following the baseline phase, the Fatigue Group completed a shoulder fatigue protocol, while Controls took a 10-minute break. Thereafter, the reaching task was repeated. Upper limb kinematic (joint angles and excursions) and spatiotemporal (speed and accuracy) data were collected during the reaching task. Electromyographic activity of the anterior and middle deltoids were also collected to characterize fatigue. Two-way repeated-measures ANOVA were performed to determine the effects of Time, Group and of the interaction between these factors.

RESULTS

The Fatigue group showed decreased mean median power frequency and increased electromyographic amplitudes of the anterior deltoid (p < 0.05) following the fatigue protocol. Less glenohumeral elevation, increased trunk flexion and rotation and sternoclavicular elevation were also observed in the Fatigue group (Group x Time interaction, p < 0.05). The Control group improved their movement speed and accuracy in post-experimental phase, while the Fatigue group showed a decrease of movement speed and no accuracy improvement (Group x Time interaction, p < 0.05).

CONCLUSION

In a fatigued state, changes in movement strategy were observed during the reaching task, including increased trunk and sternoclavicular movements and less glenohumeral movement. Performance was altered as shown by the lack of accuracy improvement over time and a decrease in movement speed in the Fatigue group.

Effects of different fatigue locations on upper body kinematics and inter-joint coordination in a repetitive pointing task

Studies have shown that muscle fatigue can lead to posture, joint angle, inter-joint coordination and variability alterations. However, the three-dimensional kinematic effects of localized muscular fatigue on a multijoint movement remain unclear. Healthy young adults (N = 17, 10 females) performed a standing repetitive pointing task when they were non-fatigued, and after localized muscle fatigue was induced at the elbow, the shoulder, and the trunk using isometric protocols performed until exhaustion. Joint angles and angular standard deviation (SD) of trunk, shoulder and elbow, and continuous relative phase (CRP) and CRP SD between trunk and shoulder, and shoulder and elbow were computed and compared between fatigue conditions. Results showed that trunk lateral flexion SD increased after fatigue of the elbow (0.1°, p = 0.04), shoulder (0.1°, p = 0.04) and trunk (0.1°, p<0.01). However, fatigue at different muscles brought different kinematic changes. Shoulder fatigue induced the greatest overall changes, with angular changes at all three joints. Trunk fatigue increased the shoulder horizontal abduction SD, elbow flexion SD and trunk-shoulder CRP. Elbow fatigue induced angular changes at trunk, shoulder and elbow, but did not affect CRP or CRP SD. This study highlights the crucial role of trunk variability in compensating for localized muscle fatigue during a repetitive upper limb task performed while standing.

Algorithmically detectable directional changes in upper extremity motion indicate substantial myoelectric shoulder muscle fatigue during a repetitive manual task

Repetitive workplace tasks are associated with fatigue-induced changes to shoulder muscular strategies, potentially altering kinematics and elevating susceptibility to tissue overexposures. Accessible and reliable methods to detect shoulder muscle fatigue in the workplace are therefore valuable. Detectable changes in joint motion may provide a plausible fatigue identification method. In this investigation, the onset of the first kinematic changes, as identified by a symbolic motion representation (SMSR) algorithm, and the onset of substantial surface electromyography (sEMG) mean power frequency (MPF) fatigue were not significantly different, both occurring around 10% of task duration. This highlights the potential utility of SMSR identified directional changes in joint motion during repetitive tasks as a cue of substantial muscle fatigue, enabling ergonomics responses that can mitigate shoulder muscular fatigue accumulation and its associated deleterious physical effects. Practitioner Summary: The onset of substantial muscle fatigue during a repetitive dynamic task was assessed using kinematics and myoelectric-based techniques. Algorithmically detectable directional changes in upper extremity joint motion occurred with the onset of substantial muscle fatigue, highlighting the potential of this as a useful approach for workplace fatigue identification.

Inter-joint coordination changes during and after muscle fatigue

People produce multi-joint movements by organizing many degrees of freedom into a few major covarying relationships, indicating a high level of inter-joint coordination. These relationships can be identified using data decomposition analyses (e.g. principal components analysis, non-negative matrix factorization). The purpose of this study was to determine how movement coordination changes during muscle fatigue by analyzing the covariance structure of multi-joint movements. Sixteen (16) healthy adults completed a continuous, timed ratcheting task with the right arm for three 1-min intervals before, during, and after an intermittent shoulder fatigue protocol. Joint angles from the right arm and trunk were tracked for subsequent principal components analysis. Principal component waveforms were constructed from the original joint angles, and changes in the waveforms during fatigue were assessed using cross-correlations. The variance explained by the first four principal components reached a maximum of 90.5% in the second minute of the pre-test and decreased to a minimum of 86.0% in the last minute of fatigue (p = .033). In the last minute of the post-test, explained variance (87.1%) did not differ from any other pre, fatigue, or post-test time point (p > .23). These results suggest that inter-joint coordination decreased during fatigue. Changes in the movement patterns and principal component waveforms suggest that subjects adopted a more rigid movement strategy when fatigued. However, the rigid movement strategy was not observed during the post-test. The results suggest that people adopted a new pattern of inter-joint coordination while using novel kinematics.

Principal components analysis to characterise fatigue-related changes in technique: Application to double under jump rope

The upper extremities play an important role in managing the rope-turning technique required to perform continuous double unders. However, acute adaptions in this technique may occur as a jumper fatigues. The purpose of this study was to examine how turning technique is adapted with fatigue. Three-dimensional kinematic data of the upper extremity were collected from 10 trained athletes as they performed consecutive double unders to volitional fatigue. Time series wrist, elbow and shoulder joint angles were calculated where joint angle waveforms representing 10 unique trials from the beginning ("fresh") and end ("fatigued") of the continuous jumping protocol for all participants were analysed using principal component analysis. Participants reported stopping due to cardiovascular and shoulder muscular fatigue. From a kinematics perspective, with fatigue athletes used a more internally rotated range of motion at the shoulder, which we believe prompted a series of more distal adaptions in order to maintain rope turning, preserving consecutive double under performance. The presence of a maladaptive adaptation at the shoulder may increase the risk of developing shoulder injuries. Coaches should consider helping jumpers develop appropriate shoulder muscle endurance such that they can continue to maximise their training and proficiency, while protecting against potential fatigue-related maladaptation.

The Averaged EMGs Recorded from the Arm Muscles During Bimanual "Rowing" Movements

The main purpose was to analyze quantitatively the the average surface EMGs of the muscles that function around the elbow and shoulder joints of both arms in bimanual “rowing” movements, which were produced under identical elastic loads applied to the levers (“oars”). The muscles of PM group (“pulling” muscles: elbow flexors, shoulder extensors) generated noticeable velocity-dependent dynamic EMG components during the pulling and returning phases of movement and supported a steady-state activity during the hold phase. The muscles of RM group (“returning” muscles: elbow extensors, shoulder flexors) co-contracted with PM group during the movement phases and decreased activity during the hold phase. The dynamic components of the EMGs strongly depended on the velocity factor in both muscle groups, whereas the side and load factors and combinations of various factors acted only in PM group. Various subjects demonstrated diverse patterns of activity redistribution among muscles. We assume that central commands to the same muscles in two arms may be essentially different during execution of similar movement programs. Extent of the diversity in the EMG patterns of such muscles may reflect the subject's skilling in motor performance; on the other hand, the diversity can be connected with redistribution of activity between synergic muscles, thus providing a mechanism directed against development of the muscle fatigue.