Impedance Control of a 2-DOF Spherical 5-Bar Exoskeleton for Physical Human-Robot Interaction During Rehabilitation and Assessment.

This paper presents a novel impedance controller for THINGER (THumb INdividuating Grasp Exercise Robot), a 2-degree-of-freedom (DOF) spherical 5-bar exoskeleton designed to augment FINGER (Finger INdividuating Grasp Exercise Robot). Many rehabilitation and assessment tasks, for which THINGER is designed, are improved by rendering near-zero impedance during physical human-robot interaction (pHRI). To achieve this goal, the presented impedance controller includes several novel features. First, a reference trajectory is omitted, allowing free movements. Second, force-feedback gains are reduced near actuator limits and a saturation function limits the maximum commanded force; both allow more responsive (higher) force-feedback gains within the workspace and mitigate transient oscillations caused by external disturbances. Finally, manipulability-based directional force-feedback gains help improve rendered impedance isotropy. Validation experiments included free exploration of the workspace, following a prescribed circular thumb motion, and intentional exposure to external disturbances. The experimental results show that the presented impedance controller significantly reduces impedance to subject-initiated motion and accurately renders the desired isotropic low-impedance environment.

Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke.

OBJECTIVE: Brain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke. APPROACH: Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance. MAIN RESULTS: Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3 ± 7.5 blocks versus 3.5 ± 3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score. SIGNIFICANCE: These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.

Adaptive control with state-dependent modeling of patient impairment for robotic movement therapy.

This paper presents an adaptive control approach for robotic movement therapy that learns a state-dependent model of patient impairment. Unlike previous work, this approach uses an unstructured inertial model that depends on both the position and direction of the desired motion in the robot's workspace. This method learns a patient impairment model that accounts for movement specific disability in neuro-muscular output (such as flexion vs. extension and slow vs. dynamic tasks). Combined with assist-as-needed force decay, this approach may promote further patient engagement and participation. Using the robotic therapy device, FINGER (Finger Individuating Grasp Exercise Robot), several experiments are presented to demonstrate the ability of the adaptive control to learn state-dependent abilities.