Based on evidence from recent experiments in motor learning and neurorehabilitation, we hypothesize that three desirable features for a controller for robot-aided movement training following stroke are high mechanical compliance, the ability to assist patients in completing desired movements, and the ability to provide only the minimum assistance necessary. This paper presents a novel controller that successfully exhibits these characteristics. The controller uses a standard model-based, adaptive control approach in order to learn the patient's abilities and assist in completing movements while remaining compliant. Assistance-as-needed is achieved by adding a novel force reducing term to the adaptive control law, which decays the force output from the robot when errors in task execution are small. Several tests are presented using the upper extremity robotic therapy device named Pneu-WREX to evaluate the performance of the adaptive, ldquoassist-as-neededrdquo controller with people who have suffered a stroke. The results of these experiments illustrate the ldquoslackingrdquo behavior of human motor control: given the opportunity, the human patient will reduce his or her output, letting the robotic device do the work for it. The experiments also demonstrate how including the ldquoassist-as-neededrdquo modification in the controller increases participation from the motor system.

Background . Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning. Objective . To determine the therapeutic effects of high and low levels of robotic assistance during finger training. Methods . We designed a protocol that varied the amount of robotic assistance while controlling the number, amplitude, and exerted effort of training movements. Participants (n = 30) with a chronic stroke and moderate hemiparesis (average Box and Blocks Test 32 ± 18 and upper extremity Fugl-Meyer score 46 ± 12) actively moved their index and middle fingers to targets to play a musical game similar to GuitarHero 3 h/wk for 3 weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8000 movements during 9 training sessions. Results . Both groups improved significantly at the 1-month follow-up on functional and impairment-based motor outcomes, on depression scores, and on self-efficacy of hand function, with no difference between groups in the primary endpoint (change in Box and Blocks). High assistance boosted motivation, as well as secondary motor outcomes (Fugl-Meyer and Lateral Pinch Strength)—particularly for individuals with more severe finger motor deficits. Individuals with impaired finger proprioception at baseline benefited less from the training. Conclusions . Robot-assisted training can promote key psychological outcomes known to modulate motor learning and retention. Furthermore, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model.

Objective To test the hypothesis that, in the context of robotic therapy designed to enhance proprioceptive feedback via a Hebbian model, integrity of both somatosensory and motor systems would be important in understanding interparticipant differences in treatment-related motor gains. Methods In 30 patients with chronic stroke, behavioral performance, neural injury, and neural function were quantified for somatosensory and motor systems. Patients then received a 3-week robot-based therapy targeting finger movements with enhanced proprioceptive feedback. Results Hand function improved after treatment (Box and Blocks score increase of 2.8 blocks, p = 0.001) but with substantial variability: 9 patients showed improvement exceeding the minimal clinically important difference (6 blocks), while 8 patients (all of whom had >2-SD greater proprioception deficit compared to 25 healthy controls) showed no improvement. In terms of baseline behavioral assessments, a somatosensory measure (finger proprioception assessed robotically) best predicted treatment gains, outperforming all measures of motor behavior. When the neural basis underlying variability in treatment response was examined, somatosensory-related variables were again the strongest predictors. A multivariate model combining total sensory system injury and sensorimotor cortical connectivity (between ipsilesional primary motor and secondary somatosensory cortices) explained 56% of variance in treatment-induced hand functional gains (p = 0.002). Conclusions Measures related to the somatosensory network best explained interparticipant differences in treatment-related hand function gains. These results underscore the importance of baseline somatosensory integrity for improving hand function after stroke and provide insights useful for individualizing rehabilitation therapy. ClinicalTrials.gov identifier: NCT02048826.

Objectives Robot-assisted movement training can help individuals with stroke reduce arm and hand impairment, but robot therapy is typically only about as effective as conventional therapy. Refining the way that robots assist during training may make them more effective than conventional therapy. Here, the authors measured the therapeutic effect of a robot that required individuals with a stroke to achieve virtual tasks in three dimensions against gravity. Design The robot continuously estimated how much assistance patients needed to perform the tasks and provided slightly less assistance than needed to reduce patient slacking. Individuals with a chronic stroke (n = 26; baseline upper limb Fugl-Meyer score, 23 ± 8) were randomized into two groups and underwent 24 one-hour training sessions over 2 mos. One group received the assist-as-needed robot training and the other received conventional tabletop therapy with the supervision of a physical therapist. Results Training helped both groups significantly reduce their motor impairment, as measured by the primary outcome measure, the Fugl-Meyer score, but the improvement was small (3.0 ± 4.9 points for robot therapy vs. 0.9 ± 1.7 for conventional therapy). There was a trend for greater reduction for the robot-trained group (P = 0.07). The robot group largely sustained this gain at the 3-mo follow-up. The robot-trained group also experienced significant improvements in Box and Blocks score and hand grip strength, whereas the control group did not, but these improvements were not sustained at follow-up. In addition, the robot-trained group showed a trend toward greater improvement in sensory function, as measured by the Nottingham Sensory Test (P = 0.06). Conclusions These results suggest that in patients with chronic stroke and moderate-severe deficits, assisting in three-dimensional virtual tasks with an assist-as-needed controller may make robotic training more effective than conventional tabletop training.

This paper describes the design and preliminary testing of FINGER (Finger Individuating Grasp Exercise Robot), a device for assisting in finger rehabilitation after neurologic injury. We developed FINGER to assist stroke patients in moving their fingers individually in a naturalistic curling motion while playing a game similar to Guitar Hero® a. The goal was to make FINGER capable of assisting with motions where precise timing is important.

This paper describes the development of a pneumatic robot for functional movement training of the arm and hand after stroke. The device is based on the Wilmington Robotic Exoskeleton (WREX), a passive, mobile arm support developed for children with arm weakness caused by a debilitative condition. Previously, we scaled WREX for use by adults, instrumented it with potentiometers, and incorporated a simple grip strength sensor. The resulting passive device (Training WREX or "T-WREX") allows individuals with severe motor impairment to practice functional movements (reaching, eating, and washing) in a simple virtual reality environment called Java Therapy 2.0. However, the device is limited since it can only apply a fixed pattern of assistive forces to the arm. In addition, its gravity balance function does not restore full range of motion. Therefore, we are also developing a robotic version of WREX named Pneu-WREX, which can apply a wide range of forces to the arm during naturalistic movements. Pneu-WREX uses pneumatic actuators, non-linear force control, and passive counter-balancing to allow application of a wide range of forces during naturalistic upper extremity movements. Besides a detailed description of the mechanical design and kinematics of Pneu-WREX, we present results from a survey of 29 therapists on the use of such a robotic device.