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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]

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation. [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]

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. [ABSTRACT FROM AUTHOR]

We routinely have physical interactions with others, whether it be handing someone a glass of water or jointly moving a heavy object together. These sensorimotor interactions between humans typically rely on visual feedback and haptic feedback. Recent single-participant studies have highlighted that the unique noise and time delays of each sense must be considered to estimate the state, such as the position and velocity, of one's own movement. However, we know little about how visual feedback and haptic feedback are used to estimate the state of another person. Here, we tested how humans utilize visual feedback and haptic feedback to estimate the state of their partner during a collaborative sensorimotor task. Across two experiments, we show that visual feedback dominated haptic feedback during collaboration. Specifically, we found that visual feedback led to comparatively lower task-relevant movement variability, smoother collaborative movements, and faster trial completion times. We also developed an optimal feedback controller that considered the noise and time delays of both visual feedback and haptic feedback to estimate the state of a partner. This model was able to capture both lower task-relevant movement variability and smoother collaborative movements. Taken together, our empirical and modeling results support the idea that visual accuracy is more important than haptic speed to perform state estimation of a partner during collaboration. [ABSTRACT FROM AUTHOR]

The naturally occurring variability in our movements often poses a significant challenge when attempting to produce precise and accurate actions, which is readily evident when playing a game of darts. Two differing, yet potentially complementary, control strategies that the sensorimotor system may use to regulate movement variability are impedance control and feedback control. Greater muscular co-contraction leads to greater impedance that acts to stabilize the hand, while visuomotor feedback responses can be used to rapidly correct for unexpected deviations when reaching toward a target. Here, we examined the independent roles and potential interplay of impedance control and visuomotor feedback control when regulating movement variability. Participants were instructed to perform a precise reaching task by moving a cursor through a narrow visual channel. We manipulated cursor feedback by visually amplifying movement variability and/or delaying the visual feedback of the cursor. We found that participants decreased movement variability by increasing muscular co-contraction, aligned with an impedance control strategy. Participants displayed visuomotor feedback responses during the task but, unexpectedly, there was no modulation between conditions. However, we did find a relationship between muscular co-contraction and visuomotor feedback responses, suggesting that participants modulated impedance control relative to feedback control. Taken together, our results highlight that the sensorimotor system modulates muscular co-contraction, relative to visuomotor feedback responses, to regulate movement variability and produce accurate actions. NEW & NOTEWORTHY The sensorimotor system has the constant challenge of dealing with the naturally occurring variability in our movements. Here, we investigated the potential roles of muscular co-contraction and visuomotor feedback responses to regulate movement variability. When we visually amplified movements, we found that the sensorimotor system primarily uses muscular co-contraction to regulate movement variability. Interestingly, we found that muscular co-contraction was modulated relative to inherent visuomotor feedback responses, suggesting an interplay between impedance and feedback control. [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]

The timing of motor commands is critical for task performance. A well-known example is rapidly raising the arm while standing upright. Here, reaction forces from the arm movement to the body are countered by leg and trunk muscle activity starting before any sensory feedback from the perturbation and often before the onset of arm muscle activity. Despite decades of research on the patterns, modifiability, and neural basis of these "anticipatory postural adjustments," it remains unclear why asynchronous motor commands occur. Simple accuracy considerations appear unlikely since temporally advanced motor commands displace the body from its initial position. Effort is a credible and overlooked factor that has successfully explained coordination patterns of many behaviors including gait and reaching. We provide the first use of optimal control to address this question. Feedforward commands were applied to a body mass mechanically linked to a rapidly moving limb mass. We determined the feedforward actions with the lowest cost according to an explicit criterion, accuracy alone versus accuracy þ effort. Accuracy costs alone led to synchronous activation of the body and limb controllers. Adding effort to the cost resulted in body commands preceding limb commands. This sequence takes advantage of the body's momentum in one direction to counter the limb's reaction force in the opposite direction, allowing a lower peak command and lower integral. With a combined accuracy þ effort cost, temporal advancement was further impacted by various task goals and plant dynamics, replicating previous findings and suggesting further studies using optimal control principles. [ABSTRACT FROM AUTHOR]

Recent work suggests that the rate of learning in sensorimotor adaptation is likely not fixed, but rather can change based on previous experience. One example is savings, a commonly observed phenomenon whereby the relearning of a motor skill is faster than the initial learning. Sensorimotor adaptation is thought to be driven by sensory prediction errors, which are the result of a mismatch between predicted and actual sensory consequences. It has been proposed that during motor adaptation the generation of sensory prediction errors engages two processes (fast and slow) that differ in learning and retention rates. We tested the idea that a history of errors would influence both the fast and slow processes during savings. Participants were asked to perform the same force field adaptation task twice in succession. We found that adaptation to the force field a second time led to increases in estimated learning rates for both fast and slow processes. While it has been proposed that savings is explained by an increase in learning rate for the fast process, here we observed that the slow process also contributes to savings. Our work suggests that fast and slow adaptation processes are both responsive to a history of error and both contribute to savings. NEW & NOTEWORTHY We studied the underlying mechanisms of savings during motor adaptation. Using a two-state model to represent fast and slow processes that contribute to motor adaptation, we found that a history of error modulates performance in both processes. While previous research has attributed savings to only changes in the fast process, we demonstrated that an increase in both processes is needed to account for the measured behavioral data. [ABSTRACT FROM AUTHOR]

At least two distinct processes have been identified by which motor commands are adapted according to movement-related feedback: reward-based learning and sensory error-based learning. In sensory error-based learning, mappings between sensory targets and motor commands are recalibrated according to sensory error feedback. In reward-based learning, motor commands are associated with subjective value, such that successful actions are reinforced. We designed two tasks to isolate reward- and sensory error-based motor adaptation, and we used electroencephalography in humans to identify and dissociate the neural correlates of reward and sensory error feedback processing. We designed a visuomotor rotation task to isolate sensory error-based learning that was induced by altered visual feedback of hand position. In a reward learning task, we isolated reward-based learning induced by binary reward feedback that was decoupled from the visual target. A fronto-central event-related potential called the feedback-related negativity (FRN) was elicited specifically by binary reward feedback but not sensory error feedback. A more posterior component called the P300 was evoked by feedback in both tasks. In the visuomotor rotation task, P300 amplitude was increased by sensory error induced by perturbed visual feedback and was correlated with learning rate. In the reward learning task, P300 amplitude was increased by reward relative to nonreward and by surprise regardless of feedback valence. We propose that during motor adaptation the FRN specifically reflects a reward-based learning signal whereas the P300 reflects feedback processing that is related to adaptation more generally. NEW & NOTEWORTHY We studied the event-related potentials evoked by feedback stimuli during motor adaptation tasks that isolate reward- and sensory error-based learning mechanisms. We found that the feedback-related negativity was specifically elicited by binary reward feedback, whereas the P300 was observed in both tasks. These results reveal neural processes associated with different learning mechanisms and elucidate which classes of errors, from a computational standpoint, elicit the feedback-related negativity and P300. [ABSTRACT FROM AUTHOR]

Consideration of previous successes and failures is essential to mastering a motor skill. Much of what we know about how humans and animals learn from such reinforcement feedback comes from experiments that involve sampling from a small number of discrete actions. Yet, it is less understood how we learn through reinforcement feedback when sampling from a continuous set of possible actions. Navigating a continuous set of possible actions likely requires using gradient information to maximize success. Here we addressed how humans adapt the aim of their hand when experiencing reinforcement feedback that was associated with a continuous set of possible actions. Specifically, we manipulated the change in the probability of reward given a change in motor action—the reinforcement gradient—to study its influence on learning. We found that participants learned faster when exposed to a steep gradient compared to a shallow gradient. Further, when initially positioned between a steep and a shallow gradient that rose in opposite directions, participants were more likely to ascend the steep gradient. We introduce a model that captures our results and several features of motor learning. Taken together, our work suggests that the sensorimotor system relies on temporally recent and spatially local gradient information to drive learning. [ABSTRACT FROM AUTHOR]

Action observation activates brain regions involved in sensory-motor control. Recent research has shown that action observation can also facilitate motor learning; observing a tutor undergoing motor learning results in functional plasticity within the motor system and gains in subsequent motor performance. However, the effects of observing motor learning extend beyond the motor domain. Converging evidence suggests that observation also results in somatosensory functional plasticity and somatosensory perceptual changes. This work has raised the possibility that the somatosensory system is also involved in motor learning that results from observation. Here we tested this hypothesis using a somatosensory perceptual training paradigm. If the somatosensory system is indeed involved in motor learning by observing, then improving subjects’ somatosensory function before observation should enhance subsequent motor learning by observing. Subjects performed a proprioceptive discrimination task in which a robotic manipulandum moved the arm, and subjects made judgments about the position of their hand. Subjects in a Trained Learning group received trial-by-trial feedback to improve their proprioceptive perception. Subjects in an Untrained Learning group performed the same task without feedback. All subjects then observed a learning video showing a tutor adapting her reaches to a left force field. Subjects in the Trained Learning group, who had superior proprioceptive acuity before observation, benefited more from observing learning than subjects in the Untrained Learning group. Improving somatosensory function can therefore enhance subsequent observation-related gains in motor learning. This study provides further evidence in favor of the involvement of the somatosensory system in motor learning by observing. NEW & NOTEWORTHY We show that improving somatosensory performance before observation can improve the extent to which subjects learn from watching others. Somatosensory perceptual training may prime the sensory-motor system, thereby facilitating subsequent observational learning. The findings of this study suggest that the somatosensory system supports motor learning by observing. This finding may be useful if observation is incorporated as part of therapies for diseases affecting movement, such as stroke. [ABSTRACT FROM AUTHOR]

Background Tree planters are at a high risk for wrist injury due to awkward postures and high wrist loads experienced during each planting cycle, specifically at shovel-ground impact. Wrist joint stiffness provides a measure that integrates postural and loading information. Objective The purpose of this study was to evaluate wrist joint stiffness requirements at the instant of shovel-ground impact during tree planting and determine if a wrist brace could alter muscular contributions to wrist joint stiffness. Method Planters simulated tree planting with and without wearing a brace on their planting arm. Surface electromyography (sEMG) from six forearm muscles and wrist kinematics were collected and used to calculate muscular contributions to joint rotational stiffness about the wrist. Results Wrist joint stiffness increased with brace use, an unanticipated and negative consequence of wearing a brace. As a potential benefit, planters achieved a more neutrally oriented wrist angle about the flexion/extension axis, although a less neutral wrist angle about the ulnar/radial axis was observed. Muscle activity did not change between conditions. Conclusion The joint stiffness analysis, combining kinematic and sEMG information in a biologically relevant manner, revealed clear limitations with the interface between the brace grip and shovel handle that jeopardized the prophylactic benefits of the current brace design. This limitation was not as evident when considering kinematics and sEMG data independently. Application A neuromechanical model (joint rotational stiffness) enhanced our ability to evaluate the brace design relative to kinematic and sEMG parameter-based metrics alone. [ABSTRACT FROM AUTHOR]

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback. [ABSTRACT FROM AUTHOR]

The human sensorimotor system is routinely capable of making accurate predictions about an object’s weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy. NEW & NOTEWORTHY Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems. [ABSTRACT FROM AUTHOR]

The minimum intervention principle and the uncontrolled manifold hypothesis state that our nervous system only responds to force perturbations and sensorimotor noise if they affect task success. This idea has been tested in muscle and joint coordinate frames and more recently using workspace redundancy (e.g., reaching to large targets). However, reaching studies typically involve spatial and or temporal constraints. Constrained reaches represent a small proportion of movements we perform daily and may limit the emergence of natural behavior. Using more relaxed constraints, we conducted two reaching experiments to test the hypothesis that humans respond to task-relevant forces and ignore task-irrelevant forces. We found that participants responded to both task-relevant and -irrelevant forces. Interestingly, participants experiencing a task-irrelevant force, which simply pushed them into a different area of a large target and had no bearing on task success, changed their movement trajectory prior to being perturbed. These movement trajectory changes did not counteract the task-irrelevant perturbations, as shown in previous research, but rather were made into new areas of the workspace. A possible explanation for this behavior change is that participants were engaging in active exploration. Our data have implications for current models and theories on the control of biological motion. [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]

Research suggests that the knee joint may be dependent on an individual muscle's translational stiffness (KT) of the surrounding musculature to prevent or compensate for ligament tearing. Our primary goal was to develop an equation that calculates KT. We successfully derived such an equation that requires as input: a muscle's coordinates, force, and stiffness acting along its line of action. This equation can also be used to estimate the total joint muscular KT, in three orthogonal axes (AP: anterior-posterior; SI: superior-inferior; ML: medial-lateral), by summating individual muscle KT contributions for each axis. We then compared the estimates of our equation, using a commonly used knee model as input, to experimental data. Our total muscular KT predictions (44.0 N/mm), along the anterior/posterior axis (AP), matched the experimental data (52.2 N/mm) and was well within the expected variability (22.6 N/mm). We then estimated the total and individual muscular KT in two postures (0 deg and 90 deg of knee flexion), with muscles mathematically set to full activation. For both postures, total muscular KT was greatest along the SI-axis. The extensors provided the greatest KT for each posture and axis. Finally, we performed a sensitivity analysis to explore the influence of each input on the equation. It was found that pennation angle had the largest effect on SI KT, while muscle line of action coordinates largely influenced AP and ML muscular KT. This equation can be easily embedded within biomechanical models to calculate the individual and total muscular KT for any joint. [ABSTRACT FROM AUTHOR]