Paul F. M. J. Verschure

Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation

BackgroundStroke is a frequent cause of adult disability that can lead to enduring impairments. However, given the life-long plasticity of the brain one could assume that recovery could be facilitated by the harnessing of mechanisms underlying neuronal reorganization. Currently it is not clear how this reorganization can be mobilized. Novel technology based neurorehabilitation techniques hold promise to address this issue. Here we describe a Virtual Reality (VR) based system, the Rehabilitation Gaming System (RGS) that is based on a number of hypotheses on the neuronal mechanisms underlying recovery, the structure of training and the role of individualization. We investigate the psychometrics of the RGS in stroke patients and healthy controls.MethodsWe describe the key components of the RGS and the psychometrics of one rehabilitation scenario called Spheroids. We performed trials with 21 acute/subacute stroke patients and 20 healthy controls to study the effect of the training parameters on task performance. This allowed us to develop a Personalized Training Module (PTM) for online adjustment of task difficulty. In addition, we studied task transfer between physical and virtual environments. Finally, we assessed the usability and acceptance of the RGS as a rehabilitation tool.ResultsWe show that the PTM implemented in RGS allows us to effectively adjust the difficulty and the parameters of the task to the user by capturing specific features of the movements of the arms. The results reported here also show a consistent transfer of movement kinematics between physical and virtual tasks. Moreover, our usability assessment shows that the RGS is highly accepted by stroke patients as a rehabilitation tool.ConclusionsWe introduce a novel VR based paradigm for neurorehabilitation, RGS, which combines specific rehabilitative principles with a psychometric evaluation to provide a personalized and automated training. Our results show that the RGS effectively adjusts to the individual features of the user, allowing for an unsupervised deployment of individualized rehabilitation protocols.

The state of the art in biomimetics.

Biomimetics is a research field that is achieving particular prominence through an explosion of new discoveries in biology and engineering. The field concerns novel technologies developed through the transfer of function from biological systems. To analyze the impact of this field within engineering and related sciences, we compiled an extensive database of publications for study with network-based information analysis techniques. Criteria included publications by year and journal or conference, and subject areas judged by popular and common terms in titles. Our results reveal that this research area has expanded rapidly from less than 100 papers per year in the 1990s to several thousand papers per year in the first decade of this century. Moreover, this research is having impact across a variety of research themes, spanning robotics, computer science and bioengineering. In consequence, biomimetics is becoming a leading paradigm for the development of new technologies that will potentially lead to significant scientific, societal and economic impact in the near future.

Environmentally mediated synergy between perception and behaviour in mobile robots

The notion that behaviour influences perception seems self-evident, but the mechanism of their interaction is not known. Perception and behaviour are usually considered to be separate processes. In this view, perceptual learning constructs compact representations of sensory events, reflecting their statistical properties, independently of behavioural relevance. Behavioural learning, however, forms associations between perception and action, organized by reinforcement, without regard for the construction of perception. It is generally assumed that the interaction between these two processes is internal to the agent, and can be explained solely in terms of the neuronal substrate. Here we show, instead, that perception and behaviour can interact synergistically via the environment. Using simulated and real mobile robots, we demonstrate that perceptual learning directly supports behavioural learning and so promotes a progressive structuring of behaviour. This structuring leads to a systematic bias in input sampling, which directly affects the organization of the perceptual system. This external, environmentally mediated feedback matches the perceptual system to the emerging behavioural structure, so that the behaviour is stabilized.

The hippocampal–striatal axis in learning, prediction and goal-directed behavior

The hippocampal formation and striatum subserve declarative and procedural memory, respectively. However, experimental evidence suggests that the ventral striatum, as opposed to the dorsal striatum, does not lend itself to being part of either system. Instead, it may constitute a system integrating inputs from the amygdala, prefrontal cortex and hippocampus to generate motivational, outcome-predicting signals that invigorate goal-directed behaviors. Inspired by reinforcement learning models, we suggest an alternative scheme for computational functions of the striatum. Dorsal and ventral striatum are proposed to compute outcome predictions largely in parallel, using different types of information as input. The nature of the inputs to striatum is furthermore combinatorial, and the specificity of predictions transcends the level of scalar value signals, incorporating episodic information.

Distributed adaptive control: The self-organization of structured behavior

Abstract Starting with a neural model for classical conditioning we have developed a system for robot control that is completely self-organizing. Instead of relying on predefined control rules the system will develop adapted control by interacting with its environment. We have tested this model in navigation tasks. Our results demonstrate that the level at which control models are normally defined seems to emerge out of the neural level which implements our control architecture.

Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: A randomized controlled pilot study in the acute phase of stroke using the Rehabilitation Gaming System

PURPOSE Given the incidence of stroke, the need has arisen to consider more self-managed rehabilitation approaches. A promising technology is Virtual Reality (VR). Thus far, however, it is not clear what the benefits of VR systems are when compared to conventional methods. Here we investigated the clinical impact of one such system, the Rehabilitation Gaming System (RGS), on the recovery time course of acute stroke. RGS combines concepts of action execution and observation with an automatic individualization of training. METHODS. Acute stroke patients (n = 8) used the RGS during 12 weeks in addition to conventional therapy. A control group (n = 8) performed a time matched alternative treatment, which consisted of intense occupational therapy or non-specific interactive games. RESULTS. At the end of the treatment, between-group comparisons showed that the RGS group displayed significantly improved performance in paretic arm speed that was matched by better performance in the arm subpart of the Fugl-Meyer Assessment Test and the Chedoke Arm and Hand Activity Inventory. In addition, the RGS group presented a significantly faster improvement over time for all the clinical scales during the treatment period. CONCLUSIONS. Our results suggest that rehabilitation with the RGS facilitates the functional recovery of the upper extremities and that this system is therefore a promising tool for stroke neurorehabilitation.

A model of grid cells based on a twisted torus topology

The grid cells of the rat medial entorhinal cortex (MEC) show an increased firing frequency when the position of the animal correlates with multiple regions of the environment that are arranged in regular triangular grids. Here, we describe an artificial neural network based on a twisted torus topology, which allows for the generation of regular triangular grids. The association of the activity of pre-defined hippocampal place cells with entorhinal grid cells allows for a highly robust-to-noise calibration mechanism, suggesting a role for the hippocampal back-projections to the entorhinal cortex.

An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search

Robots have been used to model nature, while nature in turn can contribute to the real-world artifacts we construct. One particular domain of interest is chemical search where a number of efforts are underway to construct mobile chemical search and localization systems. We report on a project that aims at constructing such a system based on our understanding of the pheromone communication system of the moth. Based on an overview of the peripheral processing of chemical cues by the moth and its role in the organization of behavior we emphasize the multimodal aspects of chemical search, i.e. optomotor anemotactic chemical search. We present a model of this behavior that we test in combination with a novel thin metal oxide sensor and custom build mobile robots. We show that the sensor is able to detect the odor cue, ethanol, under varying flow conditions. Subsequently we show that the standard model of insect chemical search, consisting of a surge and cast phases, provides for robust search and localization performance. The same holds when it is augmented with an optomotor collision avoidance model based on the Lobula Giant Movement Detector (LGMD) neuron of the locust. We compare our results to others who have used the moth as inspiration for the construction of odor robots.

Collision avoidance using a model of the locust LGMD neuron

Abstract The lobula giant movement detector (LGMD) system in the locust responds selectively to objects approaching the animal on a collision course. In earlier work we have presented a neural network model based on the LGMD system which shared this preference for approaching objects. We have extended this model in order to evaluate its responses in a real-world environment using a miniature mobile robot. This extended model shows reliable obstacle detection over an eight-fold range of speeds, and raises interesting questions about basic properties of the biological system.

The Combined Impact of Virtual Reality Neurorehabilitation and Its Interfaces on Upper Extremity Functional Recovery in Patients With Chronic Stroke

Background and Purpose— Although there is strong evidence on the beneficial effects of virtual reality (VR)-based rehabilitation, it is not yet well understood how the different aspects of these systems affect recovery. Consequently, we do not exactly know what features of VR neurorehabilitation systems are decisive in conveying their beneficial effects. Methods— To specifically address this issue, we developed 3 different configurations of the same VR-based rehabilitation system, the Rehabilitation Gaming System, using 3 different interface technologies: vision-based tracking, haptics, and a passive exoskeleton. Forty-four patients with chronic stroke were randomly allocated to one of the configurations and used the system for 35 minutes a day for 5 days a week during 4 weeks. Results— Our results revealed significant within-subject improvements at most of the standard clinical evaluation scales for all groups. Specifically we observe that the beneficial effects of VR-based training are modulated by the use/nonuse of compensatory movement strategies and the specific sensorimotor contingencies presented to the user, that is, visual feedback versus combined visual haptic feedback. Conclusions— Our findings suggest that the beneficial effects of VR-based neurorehabilitation systems such as the Rehabilitation Gaming System for the treatment of chronic stroke depend on the specific interface systems used. These results have strong implications for the design of future VR rehabilitation strategies that aim at maximizing functional outcomes and their retention. Clinical Trial Registration— This trial was not registered because it is a small clinical study that evaluates the feasibility of prototype devices.