Kunlin Wei

Relevance of error: what drives motor adaptation?

During motor adaptation the nervous system constantly uses error information to improve future movements. Todays mainstream models simply assume that the nervous system adapts linearly and proportionally to errors. However, not all movement errors are relevant to our own action. The environment may transiently disturb the movement production-for example, a gust of wind blows the tennis ball away from its intended trajectory. Apparently the nervous system should not adapt its motor plan in the subsequent tennis strokes based on this irrelevant movement error. We hypothesize that the nervous system estimates the relevance of each observed error and adapts strongly only to relevant errors. Here we present a Bayesian treatment of this problem. The model calculates how likely an error is relevant to the motor plant and derives an ideal adaptation strategy that leads to the most precise movements. This model predicts that adaptation should be a nonlinear function of the size of an error. In reaching experiments we found strong evidence for the predicted nonlinear strategy. The model also explains published data on saccadic gain adaptation, adaptation to visuomotor rotations, and force perturbations. Our study suggests that the nervous system constantly and effortlessly estimates the relevance of observed movement errors for successful motor adaptation.

Uncertainty of feedback and state estimation determines the speed of motor adaptation

Humans can adapt their motor behaviors to deal with ongoing changes. To achieve this, the nervous system needs to estimate central variables for our movement based on past knowledge and new feedback, both of which are uncertain. In the Bayesian framework, rates of adaptation characterize how noisy feedback is in comparison to the uncertainty of the state estimate. The predictions of Bayesian models are intuitive: the nervous system should adapt slower when sensory feedback is more noisy and faster when its state estimate is more uncertain. Here we want to quantitatively understand how uncertainty in these two factors affects motor adaptation. In a hand reaching experiment we measured trial-by-trial adaptation to a randomly changing visual perturbation to characterize the way the nervous system handles uncertainty in state estimation and feedback. We found both qualitative predictions of Bayesian models confirmed. Our study provides evidence that the nervous system represents and uses uncertainty in state estimate and feedback during motor adaptation.

Locomotion Mode Classification Using a Wearable Capacitive Sensing System

Locomotion mode classification is one of the most important aspects for the control of powered lower-limb prostheses. We propose a wearable capacitive sensing system for recognizing locomotion modes as an alternative solution to popular electromyography (EMG)-based systems, aiming to overcome drawbacks of the latter. Eight able-bodied subjects and five transtibial amputees were recruited for automatic classification of six common locomotion modes. The system measured ten channels of capacitance signals from the shank, the thigh, or both. With a phase-dependent linear discriminant analysis classifier and selected time-domain features, the system can achieve a satisfactory classification accuracy of 93.6% ±0.9% and 93.4% ±0.8% for able-bodied subjects and amputee subjects, respectively. The classification accuracy is comparable with that of EMG-based systems. More importantly, we verify that neuro-mechanical delay inherent in capacitive sensing does not affect the timeliness of classification decisions as the system, similar to EMG-based systems, can make multiple judgments during a gait cycle. Experimental results also indicate that capacitance signals from the thigh alone are sufficient for mode classification for both able-bodied and transtibial subjects. Our investigations demonstrate that capacitive sensing is a promising alternative to myoelectric sensing for real-time control of powered lower-limb prostheses.

Bayesian integration and non-linear feedback control in a full-body motor task

A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.

Adaptive changes of foot pressure in hallux valgus patients.

BACKGROUND Hallux valgus (HV) is one of the most common deformities in podiatric and orthopedic practice. Plantar pressure technology has been widely used in studying the pressure distribution in HV patients for better assessment to plan interventions. However, previous studies produced an array of controversial findings and most of them only focused on the forefoot. METHODS We examined the dynamic changes of foot pressure of the whole foot with a large-sample investigation (229 patients and 35 controls). Foot pain, which has been largely neglected previously, was used to group the participants. RESULTS Compared to healthy controls, patients had significantly higher loading of the first and second metatarsals, where the transverse arch usually collapses, and significantly less loading of the hallux. Moreover, forces in most regions reached their maximum late, indicating a slow build-up of loading. Patients shortened the loading duration on their forefoot, loaded more on the medial foot starting from early foot contact, and delayed the medial-to-lateral load transition. Notably, nearly all these changes were more pronounced in patients with pain. CONCLUSIONS Biomechanical changes in HV patients are not only caused by physical deformity but also by modified neural control strategies, possibly to alleviate discomfort and to accommodate the foot deformity. Our results suggest that dynamic evaluation of the whole foot and consideration of foot pain are necessary for the functional assessment of foot pressure in HV patients. The foot balance changes have important clinical implications.

Serotonin affects movement gain control in the spinal cord

A fundamental challenge for the nervous system is to encode signals spanning many orders of magnitude with neurons of limited bandwidth. To meet this challenge, perceptual systems use gain control. However, whether the motor system uses an analogous mechanism is essentially unknown. Neuromodulators, such as serotonin, are prime candidates for gain control signals during force production. Serotonergic neurons project diffusely to motor pools, and, therefore, force production by one muscle should change the gain of others. Here we present behavioral and pharmaceutical evidence that serotonin modulates the input–output gain of motoneurons in humans. By selectively changing the efficacy of serotonin with drugs, we systematically modulated the amplitude of spinal reflexes. More importantly, force production in different limbs interacts systematically, as predicted by a spinal gain control mechanism. Psychophysics and pharmacology suggest that the motor system adopts gain control mechanisms, and serotonin is a primary driver for their implementation in force production.

The uncertainty associated with visual flow fields and their influence on postural sway: Weber's law suffices to explain the nonlinearity of vection

When we stand upright, we integrate cues from multiple senses, such as vision and proprioception, to maintain and regulate our vertical posture. How these cues are combined has been the focus of a range of studies. These studies generally measured how subjects deviate from standing upright when confronted with a moving visual stimulus displayed in a virtual environment. Previous research had shown that uncertainty is central in such cue combination problems. Here we wanted to understand, quantitatively, how visual flow fields and uncertainty about them affect human posture. To do so, we combined experimental methods from perceptual psychophysics with methods from motor control studies. We used a two-alternative forced-choice paradigm to measure uncertainty as a function of the magnitude of a random-dot flow field and stimulus coherence. We subsequently measured movement amplitude as a function of visual stimulus parameters. In line with previous research, we find that sensorimotor behavior depends nonlinearly on the stimulus amplitude and, importantly, is affected by uncertainty. We find that this nonlinearity and uncertainty dependence is accurately predicted by standard Bayesian cue combination. Importantly, a Webers law where visual uncertainty depends on stimulus amplitude is enough to explain the nonlinear behavior.

A Noncontact Capacitive Sensing System for Recognizing Locomotion Modes of Transtibial Amputees

This paper presents a noncontact capacitive sensing system (C-Sens) for locomotion mode recognition of transtibial amputees. C-Sens detects changes in physical distance between the residual limb and the prosthesis. The sensing front ends are built into the prosthetic socket without contacting the skin. This novel signal source improves the usability of locomotion mode recognition systems based on electromyography (EMG) signals and systems based on capacitance signals obtained from skin contact. To evaluate the performance of C-Sens, we carried out experiments among six transtibial amputees with varying levels of amputation when they engaged in six common locomotive activities. The capacitance signals were consistent and stereotypical for different locomotion modes. Importantly, we were able to obtain sufficiently informative signals even for amputees with severe muscle atrophy (i.e., amputees lacking of quality EMG from shank muscles for mode classification). With phase-dependent quadratic classifier and selected feature set, the proposed system was capable of making continuous judgments about locomotion modes with an average accuracy of 96.3% and 94.8% for swing phase and stance phase, respectively (Experiment 1). Furthermore, the system was able to achieve satisfactory recognition performance after the subjects redonned the socket (Experiment 2). We also validated that C-Sens was robust to load bearing changes when amputees carried 5-kg weights during activities (Experiment 3). These results suggest that noncontact capacitive sensing is capable of circumventing practical problems of EMG systems without sacrificing performance and it is, thus, promising for automatic recognition of human motion intent for controlling powered prostheses.

A Wearable Plantar Pressure Measurement System: Design Specifications and First Experiments with an Amputee

In this paper, we present a wearable plantar pressure measurement system for locomotion mode recognition. The proposed system is implemented with four force sensors in each shoe to measure different given position pressure. By phase-dependent pattern recognition, we get reliable classification results of the six investigated modes for a below-knee amputee subject. The satisfactory recognition performances show the prospect of the integration of the proposed system with powered prostheses used for lower-limb amputees.

The nervous system uses nonspecific motor learning in response to random perturbations of varying nature.

We constantly make small errors during movement and use them to adapt our future movements. Movement experiments often probe this error-driven learning by perturbing movements and analyzing the after-effects. Past studies have applied perturbations of varying nature such as visual disturbances, position- or velocity-dependent forces and modified inertia properties of the limb. However, little is known about how the specific nature of a perturbation influences subsequent movements. For a single perturbation trial, the nature of a perturbation may be highly uncertain to the nervous system, given that it receives only noisy information. One hypothesis is that the nervous system can use this rough estimate to partially correct for the perturbation on the next trial. Alternatively, the nervous system could ignore uncertain information about the nature of the perturbation and resort to a nonspecific adaptation. To study how the brain estimates and responds to incomplete sensory information, we test these two hypotheses using a trial-by-trial adaptation experiment. On each trial, the nature of the perturbation was chosen from six distinct types, including a visuomotor rotation and different force fields. We observed that corrective forces aiming to oppose the perturbation in the following trial were independent of the nature of the perturbation. Our results suggest that the nervous system uses a nonspecific strategy when it has high uncertainty about the nature of perturbations during trial-by-trial learning.