Rehabilitation robots have the potential to greatly improve motor rehabilitation. However, the patient must be properly motivated to actively participate in therapy. Several strategies have been suggested to improve patient motivation, but one element has not yet been explored: playing with other people. We designed a two-player rehabilitation game played by two people using two ARMin III robots. We tested three game modes: single-player (competing against a computer), competitive (competing against a human), and cooperative (cooperating with a human against a computer). All modes were played by 24 healthy subjects who filled out questionnaires about their personality and in-game motivation. Almost all subjects preferred playing the two-player game modes to the single-player one, as they enjoyed being able to talk and interact with another person. However, there were two distinct player groups. One group liked the competitive mode but not the cooperative mode while the other liked the cooperative but not the competitive mode. Subjects who liked the competitive mode also put more effort into it. Finally, subjects' personalities partially predicted what mode they would like. This emphasizes that two-player rehabilitation games have advantages over single-player ones, but that the right game needs to be chosen for each subject. An extended patient study is planned for the near future.

In an affective feedback loop, the computer maps various measurements to affective variables such as enjoyment, then adapts its behavior based on the recognized affects. The affect recognition is never perfect, and its accuracy (percentage of times the correct affective state is recognized) depends on many factors. However, it is unclear how this accuracy relates to the overall user experience. As recognition accuracy is difficult to control in a real affective feedback loop, we describe a method of simulating recognition accuracy in a game where difficulty is increased or decreased after each round. The game was played by 261 participants at different simulated recognition accuracies. Participants reported their satisfaction with the recognition algorithm as well as their overall game experience. We observed that in such a game, the affective feedback loop must adapt game difficulty with an accuracy of at least 80 percent to be accepted by users. Furthermore, users who do not enjoy the game are likely to stop playing it rather than continue playing and report low enjoyment. However, the acceptable recognition accuracy may not generalize to other contexts, and studies of affect recognition accuracies in other applications are needed.