Using a tablet to understand the spatial and temporal characteristics of complex upper limb movements in chronic stroke.

Robotic devices are commonly used to quantify sensorimotor function of the upper limb after stroke; however, the availability and cost of such devices make it difficult to facilitate implementation in clinical environments. Tablets (e.g.

Alternatives to Friction Coefficient: Fine Touch Perception Relies on Frictional Instabilities.

Fine touch perception is often correlated to material properties and friction coefficients, but the inherent variability of human motion has led to low correlations and contradictory findings. Instead, we hypothesized that humans use frictional instabilities to discriminate between objects. We constructed a set of coated surfaces with physical differences which were imperceptible by touch but created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding.

Characterizing the Sensing Response of Carbon Nanocomposite-Based Wearable Sensors on Elbow Joint Using an End Point Robot and Virtual Reality.

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.

Punishment Leads to Greater Sensorimotor Learning But Less Movement Variability Compared to Reward.

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.

Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds.

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.

Validating the measurement of upper limb sensorimotor behavior utilizing a tablet in neurologically intact controls and individuals with chronic stroke.

Background: Intact sensorimotor function of the upper extremity is essential for successfully performing activities of daily living. After a stroke, upper limb function is often compromised and requires rehabilitation. To develop appropriate rehabilitation interventions, sensitive and objective assessments are required.

Visual accuracy dominates over haptic speed for state estimation of a partner during collaborative sensorimotor interactions.

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.

The nervous system tunes sensorimotor gains when reaching in variable mechanical environments.

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.

The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability.

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.

Failure induces task-irrelevant exploration during a stencil task.

During reward-based motor tasks, performance failure leads to an increase in movement variability along task-relevant dimensions. These increases in movement variability are indicative of exploratory behaviour in search of a better, more successful motor action. It is unclear whether failure also induces exploration along task-irrelevant dimensions that do not influence performance.

Accuracy and effort costs together lead to temporal asynchrony of multiple motor commands.

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.

Exercising choice over feedback schedules during practice is not advantageous for motor learning.

The idea that there is a self-controlled learning advantage, where individuals demonstrate improved motor learning after exercising choice over an aspect of practice compared to no-choice groups, has different causal explanations according to the OPTIMAL theory or an information-processing perspective. Within OPTIMAL theory, giving learners choice is considered an autonomy-supportive manipulation that enhances expectations for success and intrinsic motivation. In the information-processing view, choice allows learners to engage in performance-dependent strategies that reduce uncertainty about task outcomes.

Exploring Optimal Objective Function Weightings to Predict Lifting Postures Under Unfatigued and Fatigued States.

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.

Humans utilize sensory evidence of others' intended action to make online decisions.

We often acquire sensory information from another person's actions to make decisions on how to move, such as when walking through a crowded hallway. Past interactive decision-making research has focused on cognitive tasks that did not allow for sensory information exchange between humans prior to a decision. Here, we test the idea that humans accumulate sensory evidence of another person's intended action to decide their own movement.

A response surface methodology to determine the optimal objective function weightings within a multi-objective optimization digital human model used to predict postures.

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.

Rapid Feedback Responses Parallel the Urgency of Voluntary Reaching Movements.

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.

Both fast and slow learning processes contribute to savings following sensorimotor adaptation.

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.

The gradient of the reinforcement landscape influences sensorimotor learning.

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.

Neural signatures of reward and sensory error feedback processing in motor learning.

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.

Somatosensory perceptual training enhances motor learning by observing.

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.

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning.

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.

Evaluating the Ergonomic Benefit of a Wrist Brace on Wrist Posture, Muscle Activity, Rotational Stiffness, and Peak Shovel-Ground Impact Force During a Simulated Tree-Planting Task.

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.