Human balance control: Dead zones, intermittency and micro-chaos

Summary. The development of strategies to minimize the risk of falling in the elderly represents a major challenge for aging, in industrialized societies. The cor-rective movements made by humans to maintain balance are small amplitude, inter-mittent and ballistic. Small amplitude, complex oscillations (micro-chaos) frequently arise in industrial settings when a time-delayed digital processor attempts to stabilize an unstable equilibrium. Taken together these observations motivate considerations of the effects of a sensory threshold on the stabilization of an inverted pendulum by time-delayed feedback. In the resulting switching-type delay differential equations, the sensory threshold is a strong small-scale nonlinearity which has no effect on large-scale stabilization, but may produce complex, small amplitude dynamics in-cluding limit cycle oscillations and micro-chaos. A close mathematical relationship exists between a scalar model for balance control and the micro-chaotic map that arises in some models of digitally controlled machines. Surprisingly, transient, time-dependent, bounded solutions (transient stabilization) can arise even for parameter ranges where the equilibrium is asymptotically unstable. In other words the combi-nation of a sensory threshold with a time-delayed sampled feedback can increase the range of parameter values for which balance can be maintained, at least transiently. Neuro-biological observations suggest that sensory thresholds can be manipulated either passively by changing posture or actively using efferent feedback. Thus it may be possible to minimize the risk of falling by means of strategies that manipulate sensory thresholds by using physiotherapy and appropriate exercises.

Computational and Robotic Models of Human Postural Control

Currently, no bipedal robot exhibits fully human-like characteristics in terms of its postural control and movement. Current biped robots move more slowly than humans and are much less stable. Humans utilize a variety of sensory systems to maintain balance, primary among them being the visual, vestibular and proprioceptive systems. A key finding of human postural control experiments has been that the integration of sensory information appears to be dynamically regulated to adapt to changing environmental conditions and the available sensory information, a process referred to as "sensory re-weighting." In contrast, in robotics, the emphasis has been on controlling the location of the center of pressure based on proprioception, with little use of vestibular signals (inertial sensing) and no use of vision. Joint-level PD control with only proprioceptive feedback forms the core of robot standing balance control. More advanced schemes have been proposed but not yet implemented. The multiple sensory sources used by humans to maintain balance allow for more complex sensorimotor strategies not seen in biped robots, and arguably contribute to robust human balance function across a variety of environments and perturbations. Our goal is to replicate this robust human balance behavior in robots.In this work, we review results exploring sensory re-weighting in humans, through a series of experimental protocols, and describe implementations of sensory re-weighting in simulation and on a robot

Sensory and cognitive factors in multi-digit touch, and its integration with vision

Every tactile sensation – an itch, a kiss, a hug, a pen gripped between fingers, a soft fabric brushing against the skin – is experienced in relation to the body. Normally, they occur somewhere on the body’s surface – they have spatiality. This sense of spatiality is what allows us to perceive a partner’s caress in terms of its changing location on the skin, its movement direction, speed, and extent. How this spatiality arises and how it is experienced is a thriving research topic, compelled by growing interest in the nature of tactile experiences from product design to brain-machine interfaces. The present thesis adds to this flourishing area of research by examining the unified spatial quality of touch. How does distinct spatial information converge from separate areas of the body surface to give rise to our normal unified experience of touch? After explaining the importance of this question in Chapter 1, a novel paradigm to tackle this problem will be presented, whereby participants are asked to estimate the average direction of two stimuli that are simultaneously moved across two different fingerpads. This paradigm is a laboratory analogue of the more ecological task of representing the overall movement of an object held between multiple fingers. An EEG study in Chapter 2 will reveal a brain mechanism that could facilitate such aggregated perception. Next, by characterising participants’ performance not just in terms of error rates, but by considering perceptual sensitivity, bias, precision, and signal weighting, a series of psychophysical experiments will show that this aggregation ability differs for within- and between-hand perception (Chapter 3), is independent from somatotopically-defined circuitry (Chapter 4) and arises after proprioceptive input about hand posture is accounted for (Chapter 5). Finally, inspired by the demand for integrated tactile and visual experience in virtual reality and the potential of tactile interface to aid navigation, Chapter 6 will examine the contribution of tactile spatiality on visual spatial experience. Ultimately, the present thesis will reveal sensory factors that limit precise representation of concurrently occurring dynamic tactile events. It will point to cognitive strategies the brain may employ to overcome those limitations to tactually perceive coherent objects. As such, this thesis advances somatosensory research beyond merely examining the selectivity to and discrimination between experienced tactile inputs, to considering the unified experience of touch despite distinct stimulus elements. The findings also have practical implications for the design of functional tactile interfaces

Noise and vestibular perception of passive self-motion

Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber’s law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation

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

A Perspective on Human Movement Variability With Applications in Infancy Motor Development

Movement variability is considered essential to typical motor development. However, multiple theoretical perspectives and measurement tools have limited interpretation of the importance of movement variability in biological systems. The complementary use of linear and nonlinear measures have recently allowed for the evaluation of not only the magnitude of variability but also the temporal structure of variability. As a result, the theoretical model of optimal movement variability was introduced. The model suggests that the development of healthy and highly adaptable systems relies on the achievement of an optimal state of variability. Alternatively, abnormal development may be characterized by a narrow range of behaviors, some of which may be rigid, inflexible, and highly predictable or, on the contrary, random, unfocused, and unpredictable. In the present review, this theoretical model is described as it relates to motor development in infancy and specifically the development of sitting posture

Phenotype Extraction: Estimation and Biometrical Genetic Analysis of Individual Dynamics

Within-person data can exhibit a virtually limitless variety of statistical patterns, but it can be difficult to distinguish meaningful features from statistical artifacts. Studies of complex traits have previously used genetic signals like twin-based heritability to distinguish between the two. This dissertation is a collection of studies applying state-space modeling to conceptualize and estimate novel phenotypic constructs for use in psychiatric research and further biometrical genetic analysis. The aims are to: (1) relate control theoretic concepts to health-related phenotypes; (2) design statistical models that formally define those phenotypes; (3) estimate individual phenotypic values from time series data; (4) consider hierarchical methods for biometrical genetic analysis of individual phenotypic variation