Making neurorehabilitation fun: Multiplayer training via damping forces balancing differences in skill levels

Abstract

Multiplayer environments are thought to increase the training intensity in robot-aided rehabilitation therapy after stroke. We developed a haptic-based environment to investigate the dynamics of two-player training performing time-constrained reaching movements using the ARMin rehabilitation robot. We implemented a challenge level adaptation algorithm that controlled a virtual damping coefficient to reach a desired success rate. We tested the algorithm's effectiveness in regulating the success rate during game play in a simulation with computer-controlled players, in a feasibility study with six unimpaired players, and in a single session with one stroke patient. The algorithm demonstrated its capacity to adjust the damping coefficient to reach three levels of success rate (low [50%], moderate [70%], and high [90%]) during singleplayer and multiplayer training. For the patient - tested in single-player mode at the moderate success rate only - the algorithm showed also promising behavior. Results of the feasibility study showed that to increase the player's willingness to play at a more challenging task condition, the effect of the challenge level adaptation - regardless of being played in single player or multiplayer mode - might be more important than the provision of multiplayer setting alone. Furthermore, the multiplayer setting tends to be a motivating and encouraging therapy component. Based on these results we will optimize and expand the multiplayer training platform and further investigate multiplayer settings in stroke therapy.