Cooperative Goal Generation for Reaching Tasks in Robot-Assisted Rehabilitation.

Robot-assisted neurorehabilitation requires automated generation of goal positions for reaching tasks in functional movement therapy. In state-of-the-art solutions, these positions are determined by a motivational therapy game either through constraints on the end-effector (2D or 3D games), or individual arm joints (1D games). Consequently, these positions cannot be adapted to the patients' specific needs by the therapist, and the effectiveness of the training is reduced. We solve this issue by generating goal positions using Gaussian Mixture Models and probability density maps based on the active range of motion of the patient and desired activities, while being compliant with existing game constraints. Therapists can modify the goal generation via an intuitive difficulty and activity parameter. The pipeline was tested on the upper-limb exoskeleton ANYexo 2.0. We have shown that the range of motion exploration rate could be altered from 0.39% to 5.9% per task and that our method successfully generated a sequence of reaching tasks that matched the range of motion of the selected activity, up to an inlier accuracy of 78.9%. Results demonstrate that the responsibilities of the therapy game (i.e., motivating the patient) and the therapists (i.e., individualizing the training) could be distributed properly. We believe that with our pipeline, effective cooperation between the involved agents is achieved, and the provided therapy can be improved.

ARMStick - An Intuitive Therapist Interface for Upper-Limb Rehabilitation Robots.

Currently, therapists struggle with interaction of rehabilitation robots due to non-intuitive interfaces. Therefore their acceptance of these robots are limited. This paper presents the development of ARMStick, a lightweight and small robotic interface in the shape of a human arm with 4 actuated and 3 unactuated joints, to facilitate the interaction between therapists and rehabilitation robots. It allows therapists to intuitively perceive joint-dependent data as recorded by rehabilitation robots, and teach poses and trajectories to individualize therapy to the patient. It's range of motion (RoM) covers the RoM of a healthy human. The device's measuring accuracy of CI 95% $ \lt \pm 0.322^{\circ}$ and movement accuracy of CI 70% $ \lt \pm 5.23^{\circ}$ lie within the confidence interval of average visual perception. A demonstration of the device to 5 therapists indicated that it could indeed improve efficiency and efficacy bottlenecks in current robot-assisted therapy. Comparison of ARMStick to two visual user interfaces showed a decrease in mean adaptation time from 15s to 5s for three arm configurations presented to the therapists.

Digital Guinea Pig: Merits and Methods of Human-in-the-Loop Simulation for Upper-Limb Exoskeletons.

Exoskeletons operate in continuous haptic interaction with a human limb. Thus, this interaction is a key factor to consider during the development of hardware and control policies for these devices. Physics simulations can complement real-world experiments for prototype validation, leading to higher efficiency in hardware and software development iterations as well as increased safety for participants and robot hardware. Here, we present a simulation framework of the full rigid-body dynamics of a coupled human and exoskeleton arm built to validate the full software stack. We present a method to model the human-robot interaction dynamics as decoupled spring-damper systems based on anthropometric data. Further, we demonstrate the application of the simulation framework to predict the closed-loop haptic-rendering performance of a 9-DOF exoskeleton in interaction with a human. The simulation was capable of simulating the closed-loop system's reaction to an impact on a haptic wall. The intrusion into the compliant walls was predicted with a relative accuracy of 6% to 13%. Admissible control gains could be predicted with an accuracy of around 14%, and higher prediction accuracy is indicated when modeling the torque tracking bandwidth of the actuators. Hence, the simulation is valuable for validating prototype software, developing intuition, and a better understanding of the complex characteristics of the coupled system dynamics, even though the quantitative prediction is limited.