Abstract-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, "assist-as-needed" controller with people who have suffered a stroke. The results of these experiments illustrate the "slacking" 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 "assist-as-needed" modification in the controller increases participation from the motor system.

BackgroundThis 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.MethodsFINGER consists of a pair of stacked single degree-of-freedom 8-bar mechanisms, one for the index and one for the middle finger. Each 8-bar mechanism was designed to control the angle and position of the proximal phalanx and the position of the middle phalanx. Target positions for the mechanism optimization were determined from trajectory data collected from 7 healthy subjects using color-based motion capture. The resulting robotic device was built to accommodate multiple finger sizes and finger-to-finger widths. For initial evaluation, we asked individuals with a stroke (n = 16) and without impairment (n = 4) to play a game similar to Guitar Hero® while connected to FINGER.ResultsPrecision design, low friction bearings, and separate high speed linear actuators allowed FINGER to individually actuate the fingers with a high bandwidth of control (−3 dB at approximately 8 Hz). During the tests, we were able to modulate the subject’s success rate at the game by automatically adjusting the controller gains of FINGER. We also used FINGER to measure subjects’ effort and finger individuation while playing the game.ConclusionsTest results demonstrate the ability of FINGER to motivate subjects with an engaging game environment that challenges individuated control of the fingers, automatically control assistance levels, and quantify finger individuation after stroke.

Objective 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 we 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 in order to reduce patient slacking. Individuals with a chronic stroke (n = 26, baseline upper extremity Fugl-Meyer score = 23 ± 8) were randomized into two groups and underwent 24 one hour training sessions over 2 months. One group received the assist-as-needed robot training and the other received conventional table top 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, versus 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 three-month follow-up. The robot-trained group also experienced significant improvements in Box and Blocks score and hand grip strength, while 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 table top training.

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 three hours/week for three weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8,000 movements during nine training sessions. Results Both groups improved significantly at the one-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. Further, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model. ClinicalTrials.gov (NCT02048826)