Monitoring functional arm movement for home-based therapy after stroke.

The goal of this project is to develop a means for individuals with stroke to practice arm movement therapy at home with remote monitoring. We previously developed a Web-based system for repetitive movement training (Java Therapy). This paper describes a new input device for the system that measures and assists in naturalistic arm movement, as well as software enhancements. The new input device is an instrumented, adult-sized version of Wilmington robotic exoskeleton (WREX), which is a five degrees-of-freedom orthosis that counterbalances the weight of the arm using elastic bands. To test the ability of the new device (Training-WREX or "T-WREX") to measure and assist in functional arm movements, we measured five chronic stroke subjects' movement ability while wearing the orthosis without gravity balance compared to wearing the orthosis with gravity balance. T-WREX's gravity balance function improved a clinical measure of arm movement (Fugl-Meyer Score), range of motion of reaching movements, and accuracy of drawing movements. Coupled with an enhanced version of Java Therapy, T-WREX will thus provide a means to assist functional arm movement training at home, either over the Web in real-time, or stand-alone with periodic communication with a remote site

Differential game theory for versatile physical human-robot interaction

The last decades have seen a surge of robots working in contact with humans. However, until now these contact robots have made little use of the opportunities offered by physical interaction and lack a systematic methodology to produce versatile behaviours. Here, we develop an interactive robot controller able to understand the control strategy of the human user and react optimally to their movements. We demonstrate that combining an observer with a differential game theory controller can induce a stable interaction between the two partners, precisely identify each other’s control law, and allow them to successfully perform the task with minimum effort. Simulations and experiments with human subjects demonstrate these properties and illustrate how this controller can induce different representative interaction strategies

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Restoring brain function after stroke - bridging the gap between animals and humans

Stroke is the leading cause of complex adult disability in the world. Recovery from stroke is often incomplete, which leaves many people dependent on others for their care. The improvement of long-term outcomes should, therefore, be a clinical and research priority. As a result of advances in our understanding of the biological mechanisms involved in recovery and repair after stroke, therapeutic opportunities to promote recovery through manipulation of poststroke plasticity have never been greater. This work has almost exclusively been carried out in preclinical animal models of stroke with little translation into human studies. The challenge ahead is to develop a mechanistic understanding of recovery from stroke in humans. Advances in neuroimaging techniques now enable us to reconcile behavioural accounts of recovery with molecular and cellular changes. Consequently, clinical trials can be designed in a stratified manner that takes into account when an intervention should be delivered and who is most likely to benefit. This approach is expected to lead to a substantial change in how restorative therapeutic strategies are delivered in patients after stroke

A Force-Feedback Exoskeleton for Upper-Limb Rehabilitation in Virtual Reality

This paper presents the design and the clinical validation of an upper-limb force-feedback exoskeleton, the L-EXOS, for robotic-assisted rehabilitation in virtual reality (VR). The L-EXOS is a five degrees of freedom exoskeleton with a wearable structure and anthropomorphic workspace that can cover the full range of motion of human arm. A specific VR application focused on the reaching task was developed and evaluated on a group of eight post-stroke patients, to assess the efficacy of the system for the rehabilitation of upper limb. The evaluation showed a significant reduction of the performance error in the reaching task (paired t-test, p < 0.02

Potential of a suite of robot/computer-assisted motivating systems for personalized, home-based, stroke rehabilitation

BACKGROUND: There is a need to improve semi-autonomous stroke therapy in home environments often characterized by low supervision of clinical experts and low extrinsic motivation. Our distributed device approach to this problem consists of an integrated suite of low-cost robotic/computer-assistive technologies driven by a novel universal access software framework called UniTherapy. Our design strategy for personalizing the therapy, providing extrinsic motivation and outcome assessment is presented and evaluated. METHODS: Three studies were conducted to evaluate the potential of the suite. A conventional force-reflecting joystick, a modified joystick therapy platform (TheraJoy), and a steering wheel platform (TheraDrive) were tested separately with the UniTherapy software. Stroke subjects with hemiparesis and able-bodied subjects completed tracking activities with the devices in different positions. We quantify motor performance across subject groups and across device platforms and muscle activation across devices at two positions in the arm workspace. RESULTS: Trends in the assessment metrics were consistent across devices with able-bodied and high functioning strokes subjects being significantly more accurate and quicker in their motor performance than low functioning subjects. Muscle activation patterns were different for shoulder and elbow across different devices and locations. CONCLUSION: The Robot/CAMR suite has potential for stroke rehabilitation. By manipulating hardware and software variables, we can create personalized therapy environments that engage patients, address their therapy need, and track their progress. A larger longitudinal study is still needed to evaluate these systems in under-supervised environments such as the home

A robotic wheelchair trainer: design overview and a feasibility study

<p>Abstract</p> <p>Background</p> <p>Experiencing independent mobility is important for children with a severe movement disability, but learning to drive a powered wheelchair can be labor intensive, requiring hand-over-hand assistance from a skilled therapist.</p> <p>Methods</p> <p>To improve accessibility to training, we developed a robotic wheelchair trainer that steers itself along a course marked by a line on the floor using computer vision, haptically guiding the driver's hand in appropriate steering motions using a force feedback joystick, as the driver tries to catch a mobile robot in a game of "robot tag". This paper provides a detailed design description of the computer vision and control system. In addition, we present data from a pilot study in which we used the chair to teach children without motor impairment aged 4-9 (n = 22) to drive the wheelchair in a single training session, in order to verify that the wheelchair could enable learning by the non-impaired motor system, and to establish normative values of learning rates.</p> <p>Results and Discussion</p> <p>Training with haptic guidance from the robotic wheelchair trainer improved the steering ability of children without motor impairment significantly more than training without guidance. We also report the results of a case study with one 8-year-old child with a severe motor impairment due to cerebral palsy, who replicated the single-session training protocol that the non-disabled children participated in. This child also improved steering ability after training with guidance from the joystick by an amount even greater than the children without motor impairment.</p> <p>Conclusions</p> <p>The system not only provided a safe, fun context for automating driver's training, but also enhanced motor learning by the non-impaired motor system, presumably by demonstrating through intuitive movement and force of the joystick itself exemplary control to follow the course. The case study indicates that a child with a motor system impaired by CP can also gain a short-term benefit from driver's training with haptic guidance.</p

Overground walking speed changes when subjected to body weight support conditions for nonimpaired and post stroke individuals

<p>Abstract</p> <p>Background</p> <p>Previous research has shown that body weight support (BWS) has the potential to improve gait speed for individuals post-stroke. However, body weight support also reduces the optimal walking speed at which energy use is minimized over the gait cycle indicating that BWS should reduce walking speed capability.</p> <p>Methods</p> <p>Nonimpaired subjects and subjects post-stroke walked at a self-selected speed over a 15 m walkway. Body weight support (BWS) was provided to subjects at 0%, 10%, 20%, 30%, and 40% of the subject's weight while they walked overground using a robotic body weight support system. Gait speed, cadence, and average step length were calculated for each subject using recorded data on their time to walk 10 m and the number of steps taken.</p> <p>Results</p> <p>When subjected to greater levels of BWS, self-selected walking speed decreased for the nonimpaired subjects. However, subjects post-stroke showed an average increase of 17% in self-selected walking speed when subjected to some level of BWS compared to the 0% BWS condition. Most subjects showed this increase at the 10% BWS level. Gait speed increases corresponded to an increase in step length, but not cadence.</p> <p>Conclusions</p> <p>The BWS training environment results in decreased self-selected walking speed in nonimpaired individuals, however self-selected overground walking speed is facilitated when provided with a small percentage of body weight support for people post-stroke.</p

Balancing the playing field: collaborative gaming for physical training.

BACKGROUND: Multiplayer video games promoting exercise-based rehabilitation may facilitate motor learning, by increasing motivation through social interaction. However, a major design challenge is to enable meaningful inter-subject interaction, whilst allowing for significant skill differences between players. We present a novel motor-training paradigm that allows real-time collaboration and performance enhancement, across a wide range of inter-subject skill mismatches, including disabled vs. able-bodied partnerships. METHODS: A virtual task consisting of a dynamic ball on a beam, is controlled at each end using independent digital force-sensing handgrips. Interaction is mediated through simulated physical coupling and locally-redundant control. Game performance was measured in 16 healthy-healthy and 16 patient-expert dyads, where patients were hemiparetic stroke survivors using their impaired arm. Dual-player was compared to single-player performance, in terms of score, target tracking, stability, effort and smoothness; and questionnaires probing user-experience and engagement. RESULTS: Performance of less-able subjects (as ranked from single-player ability) was enhanced by dual-player mode, by an amount proportionate to the partnership's mismatch. The more abled partners' performances decreased by a similar amount. Such zero-sum interactions were observed for both healthy-healthy and patient-expert interactions. Dual-player was preferred by the majority of players independent of baseline ability and subject group; healthy subjects also felt more challenged, and patients more skilled. CONCLUSION: This is the first demonstration of implicit skill balancing in a truly collaborative virtual training task leading to heightened engagement, across both healthy subjects and stroke patients

A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p