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Physical therapy is often essential for complete recovery after injury. However, a significant population of patients fail to adhere to prescribed exercise regimens. Lack of motivation and inconsistent in-person visits to physical therapy are major contributing factors to suboptimal exercise adherence, slowing the recovery process. With the advancement of virtual reality (VR), researchers have developed remote virtual rehabilitation systems with sensors such as inertial measurement units. A functional garment with an integrated wearable sensor can also be used for real-time sensory feedback in VR-based therapeutic exercise and offers affordable remote rehabilitation to patients. Sensors integrated into wearable garments offer the potential for a quantitative range of motion measurements during VR rehabilitation. In this research, we developed and validated a carbon nanocomposite-coated knit fabric-based sensor worn on a compression sleeve that can be integrated with upper-extremity virtual rehabilitation systems. The sensor was created by coating a commercially available weft knitted fabric consisting of polyester, nylon, and elastane fibers. A thin carbon nanotube composite coating applied to the fibers makes the fabric electrically conductive and functions as a piezoresistive sensor. The nanocomposite sensor, which is soft to the touch and breathable, demonstrated high sensitivity to stretching deformations, with an average gauge factor of ~35 in the warp direction of the fabric sensor. Multiple tests are performed with a Kinarm end point robot to validate the sensor for repeatable response with a change in elbow joint angle. A task was also created in a VR environment and replicated by the Kinarm. The wearable sensor can measure the change in elbow angle with more than 90% accuracy while performing these tasks, and the sensor shows a proportional resistance change with varying joint angles while performing different exercises. The potential use of wearable sensors in at-home virtual therapy/exercise was demonstrated using a Meta Quest 2 VR system with a virtual exercise program to show the potential for at-home measurements. [ABSTRACT FROM AUTHOR]

Objective: To explore whether the optimal objective function weightings change when using a digital human model (DHM) to predict origin and destination lifting postures under unfatigued and fatigued states. Background: The ability to predict human postures can depend on state-based influences (e.g., fatigue). Altering objective function weightings within a predictive DHM could improve the ability to predict tasks specific lifting postures under unique fatigue states. Method: A multi-objective optimization-based DHM was used to predict origin and destination lifting postures for ten anthropometrically scaled avatars by using different objective functions weighting combinations. Predicted and measured postures were compared to determine the root mean squared error. A response surface methodology was used to identify the optimal objective function weightings, which was found by generating the posture that minimized error between measured and predicted lifting postures. The resultant weightings were compared to determine if the optimal objective function weightings changed for different lifting postures or fatigue states. Results: Discomfort and total joint torque weightings were affected by posture (origin/destination) and fatigue state (unfatigued/fatigued); however, post-hoc differences between fatigue states and lifting postures were not sufficiently large to be detected. Weighting the discomfort objective function alone tended to predict postures that generalized well to both postures and fatigue states. Conclusion: Lift postures were optimal predicted using the minimization of discomfort objective function regardless of fatigue state. Application: Weighting the discomfort objective can predict unfatigued postures, but more research is needed to understand the optimal objective function weightings to predict postures during a fatigued state. [ABSTRACT FROM AUTHOR]

Multi-objective optimization digital human models permit users to predict postures that follow performance criteria, such as minimizing torques. Currently, it is unknown how to weight different objective functions to best predict postures. Objective one was to describe a response surface method to determine optimal objective function weightings to predict lift postures. Objective two was to evaluate the sensitivity of different error calculation methods. Our response surface approach has utility for determining optimal objective function weightings when using a digital human model to evaluate human-system interactions in early design stages. The approach was not dependent on variations in error calculation methods. [ABSTRACT FROM AUTHOR]

Empirical evidence suggests that our nervous system considers many objectives when performing various tasks. With the progression of fatigue, researchers have noted increase in both joint moment variability and muscular cocontraction during isometric force production tasks. Muscular cocontraction increases joint stability, but is metabolically costly. Thus, our nervous system must select a compromise between joint stability and energy efficiency. Interestingly, the continuous increase in cocontraction with fatigue suggests there may be a shift in the relative weighting of these objectives. Here we test the notion of dynamic objective weightings. Using multi-objective optimization, we found a shift in objective weighting that favoured joint stability at the expense of energy efficiency during fatigue. This shift was highly correlated with muscular cocontraction (R²=0.78, p < 0.001) and elbow moment variability in the time (R²=0.56, p < 0.01) and frequency (R²0=0.57, p < 0.01) domains. By considering a dynamic objective weighting we obtained strong correlations with predicted and collected muscle activity (R²=0.94, p < 0.001). [ABSTRACT FROM AUTHOR]

ABSTRACT Neuroeconomics research has shown that preference for gambling is altered by the statistical moments (mean, variance, and skew) of reward and punishment distributions. Although it has been shown that altered means can affect feedback-based decision making tasks, little is known if the variance and skew will have an effect on these tasks. To investigate, we systematically controlled the variance (high, medium, and low) and skew (negative, zero, and positive) of the punishment distributions in a modified version of the Iowa Gambling Task. The Iowa Gambling Task has been used extensively in both academic and clinical domains to understand decision making and diagnose decision making impairments. Our results show that decision making can be altered by an interaction of variance and skew. We found a significant decrease over trials in choices from the decks with high variance and asymmetrically skewed punishments and from the decks with low variance and zero skew punishments. These results indicate that punishment distribution shape alone can change human perception of what is optimal (i.e., mean expected outcome) and may help explain what guides our day-to-day decisions. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Major joints, such as the knee, shoulder, and spine, can buckle along the translational degrees-of-freedom (DoF), causing injury to ligaments and other passive tissues. Despite this, stability and impedance analyses have focused primarily on the rotational DoF. As such, mathematical models quantifying musculotendon translational stiffnesses remain limited and, to our knowledge, there are no published works that explicitly describes the interactions between DoF. Using an energy approach, we derived a six DoF stiffness tensor and provided the necessary equations needed to quantify the musculotendon stiffness of any joint. Using a knee model, we then compared the derived stiffness tensor against two commonly used measures: one that excludes translational DoF and another that excludes interactions between DoF. We found that both of these measures had large over-estimations of stiffness, particularly for the rotational DoF, compared to our derived tensor. These findings indicate that previous analyses may have found rotational DoF to be stable when they were unstable. [ABSTRACT FROM AUTHOR]

When a musician practices a new song, hitting a correct note sounds pleasant while striking an incorrect note sounds unpleasant. Such reward and punishment feedback has been shown to differentially influence the ability to learn a new motor skill. Recent work has suggested that punishment leads to greater movement variability, which causes greater exploration and faster learning. To further test this idea, we collected 102 participants over two experiments. Unlike previous work, in Experiment 1 we found that punishment did not lead to faster learning compared to reward (n = 68), but did lead to a greater extent of learning. Surprisingly, we also found evidence to suggest that punishment led to less movement variability, which was related to the extent of learning. We then designed a second experiment that did not involve adaptation, allowing us to further isolate the influence of punishment feedback on movement variability. In Experiment 2, we again found that punishment led to significantly less movement variability compared to reward (n = 34). Collectively our results suggest that punishment feedback leads to less movement variability. Future work should investigate whether punishment feedback leads to a greater knowledge of movement variability and or increases the sensitivity of updating motor actions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 IBRO. Published by Elsevier Inc. All rights reserved.)

From a baby's babbling to a songbird practising a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species' neural firing, gait patterns and reaching behaviour. The past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here, we test a fundamentally different idea-that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human reaching experiments, we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that exploratory random walk behaviour emerges by utilizing knowledge of movement variability to update intended reach aim towards recently reinforced motor actions. This mechanism leads to active and continuous exploration of the solution manifold, currently thought by prominent theories to arise passively. The ability to continually explore muscle, joint and task redundant solution manifolds is beneficial while acting in uncertain environments, during motor development or when recovering from a neurological disorder to discover and learn new motor actions.

Humans often move in the presence of mechanical disturbances that can vary in direction and amplitude throughout movement. These disturbances can jeopardize the outcomes of our actions, such as when drinking from a glass of water on a turbulent flight or carrying a cup of coffee while walking on a busy sidewalk. Here, we examine control strategies that allow the nervous system to maintain performance when reaching in the presence of mechanical disturbances that vary randomly throughout movement. Healthy participants altered their control strategies to make movements more robust against disturbances. The change in control was associated with faster reaching movements and increased responses to proprioceptive and visual feedback that were tuned to the variability of the disturbances. Our findings highlight that the nervous system exploits a continuum of control strategies to increase its responsiveness to sensory feedback when reaching in the presence of increasingly variable physical disturbances., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Optimal feedback control is a prominent theory used to interpret human motor behaviour. The theory posits that skilled actions emerge from control policies that link voluntary motor control (feedforward) with flexible feedback corrections (feedback control). It is clear the nervous system can generate flexible motor corrections (reflexes) when performing actions with different goals. We know little, however, about shared features of voluntary actions and feedback control in human movement. Here we reveal a link between the timing demands of voluntary actions and flexible responses to mechanical perturbations. In two experiments, 40 human participants (21 females) made reaching movements with different timing demands. We disturbed the arm with mechanical perturbations at movement onset (Experiment 1) and at locations ranging from movement onset to completion (Experiment 2). We used the resulting muscle responses and limb displacements as a proxy for the control policies that support voluntary reaching movements. We observed an increase in the sensitivity of elbow and shoulder muscle responses and a reduction in limb motion when the task imposed greater urgency to respond to the same perturbations. The results reveal a relationship between voluntary actions and feedback control as the limb was displaced less when moving faster in perturbation trials. Muscle responses scaled with changes in the displacement of the limb in perturbation trials within each timing condition. Across both experiments, human behaviour was captured by simulations based on stochastic optimal feedback control. Taken together, the results highlight flexible control that links sensory processing with features of human reaching movements., (Copyright © 2021 IBRO. Published by Elsevier Ltd. All rights reserved.)

An influential idea in neuroscience is that the sensory-motor system is activated when observing the actions of others [1, 2]. This idea has recently been extended to motor learning, in which observation results in sensory-motor plasticity and behavioral changes in both motor and somatosensory domains [3-9]. However, it is unclear how the brain maps visual information onto motor circuits for learning. Here we test the idea that the somatosensory system, and specifically primary somatosensory cortex (S1), plays a role in motor learning by observing. In experiment 1, we applied stimulation to the median nerve to occupy the somatosensory system with unrelated inputs while participants observed a tutor learning to reach in a force field. Stimulation disrupted motor learning by observing in a limb-specific manner. Stimulation delivered to the right arm (the same arm used by the tutor) disrupted learning, whereas left arm stimulation did not. This is consistent with the idea that a somatosensory representation of the observed effector must be available during observation for learning to occur. In experiment 2, we assessed S1 cortical processing before and after observation by measuring somatosensory evoked potentials (SEPs) associated with median nerve stimulation. SEP amplitudes increased only for participants who observed learning. Moreover, SEPs increased more for participants who exhibited greater motor learning following observation. Taken together, these findings support the idea that motor learning by observing relies on functional plasticity in S1. We propose that visual signals about the movements of others are mapped onto motor circuits for learning via the somatosensory system., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: In this study we investigate whether the nervous system is responsive to anterior-posterior shearing forces applied to the knee. Specifically, we examine whether the nervous system will increase musculotendon stiffness along the anterior-posterior degree-of-freedom via relative changes in muscle activation., Methods: While seated on a Biodex, 12 male participants performed knee extensor moments against a cuff at three different distances (265mm, 310mm, and 355mm) from the knee joint. This was done for each combination of knee angle (20°, 45°, 70°), moment (25%, 75% of maximum), and angular velocity (0°/s, 10°/s). By altering cuff position, the magnitude of the shear force could be changed while holding moment constant. We then calculated the 6-degree-of-freedom, musculotendon stiffness matrix of the knee and interpreted the eigenvalues and eigenvectors of this matrix using stiffness ellipses., Findings: Anterior-Posterior knee stiffness significantly increased with an increase in shear force at 20° and 45° of knee flexion. This coincided with significant increases in semitendinosus and vastus medialis activity., Interpretation: We found that the nervous system can significantly increase stiffness along a translational degree-of-freedom in response to translational loading, and did so by activating muscles geometrically oriented to provide stiffness along the anterior-posterior degree-of-freedom., (Published by Elsevier Ltd.)

Nonlinear dynamical techniques offer a powerful approach for the investigation of physiological time series. Multiscale entropy analyses have shown that pathological and aging systems are less complex than healthy systems and this finding has been attributed to degraded physiological control processes. A similar phenomenon may arise during fatiguing muscle contractions where surface electromyography signals undergo temporal and spectral changes that arise from the impaired regulation of muscle force production. Here we examine the affect of fatigue and contraction intensity on the short and long-term complexity of biceps brachii surface electromyography. To investigate, we used an isometric muscle fatigue protocol (parsed into three windows) and three contraction intensities (% of maximal elbow joint moment: 40%, 70% and 100%). We found that fatigue reduced the short-term complexity of biceps brachii activity during the last third of the fatiguing contraction. We also found that the complexity of surface electromyography is dependent on contraction intensity. Our results show that multiscale entropy is sensitive to muscle fatigue and contraction intensity and we argue it is imperative that both factors be considered when evaluating the complexity of surface electromyography signals. Our data contribute to a converging body of evidence showing that multiscale entropy can quantify subtle information content in physiological time series., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The purpose of this paper was to investigate total joint rotational stiffness (JRS) and relative muscle contributions to JRS with varying extensor moment demands. It was hypothesized that greater co-activation of the flexors at lower levels of moment would result in greater JRS, relative to moment demands. It was also hypothesized that the flexors would have greater relative JRS contributions at lower moment levels. Twelve male participants generated isometric extensor moments about the knee at varying intensities, during plateau and ramping (up and down) conditions. Electromyography was used to estimate individual muscle forces, which were used to calculate JRS about three orthogonal axes. Orthogonal trend analysis revealed a linear relationship (p<.001) between moment and JRS, about all axes. The vastus lateralis provided the greatest JRS about all axes. Of the flexors, the semimembranosis provided the most JRS about the flexion/extension (FE) and axial (AX) axes. However, while most muscle activity increased linearly, the gastrocnemious lateral (GL) had an interaction between condition and moment. Despite an extensor moment, the GL had minor contributions about the FE axis (1.2%), and it was postulated that this increase in activation was to stabilize about the VV axis, where its contribution was as high as 18.9%., (Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.)

We aimed to determine if the time that muscle is under loaded tension during low intensity resistance exercise affects the synthesis of specific muscle protein fractions or phosphorylation of anabolic signalling proteins. Eight men (24 ± 1 years (sem), BMI = 26.5 ± 1.0 kg m(-2)) performed three sets of unilateral knee extension exercise at 30% of one-repetition maximum strength involving concentric and eccentric actions that were 6 s in duration to failure (SLOW) or a work-matched bout that consisted of concentric and eccentric actions that were 1 s in duration (CTL). Participants ingested 20 g of whey protein immediately after exercise and again at 24 h recovery. Needle biopsies (vastus lateralis) were obtained while fasted at rest and after 6, 24 and 30 h post-exercise in the fed-state following a primed, constant infusion of l-[ring-(13)C(6)]phenylalanine. Myofibrillar protein synthetic rate was higher in the SLOW condition versus CTL after 24-30 h recovery (P < 0.001) and correlated to p70S6K phosphorylation (r = 0.42, P = 0.02). Exercise-induced rates of mitochondrial and sarcoplasmic protein synthesis were elevated by 114% and 77%, respectively, above rest at 0-6 h post-exercise only in the SLOW condition (both P < 0.05). Mitochondrial protein synthesis rates were elevated above rest during 24-30 h recovery in the SLOW (175%) and CTL (126%) conditions (both P < 0.05). Lastly, muscle PGC-1α expression was increased at 6 h post-exercise compared to rest with no difference between conditions (main effect for time, P < 0.001). These data show that greater muscle time under tension increased the acute amplitude of mitochondrial and sarcoplasmic protein synthesis and also resulted in a robust, but delayed stimulation of myofibrillar protein synthesis 24-30 h after resistance exercise.