Gorsic Maja, Darzi Ali, Novak Domen
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:640-645. doi: 10.1109/ICORR.2017.8009320.
This paper presents two different strategies for difficulty adaptation in a competitive arm rehabilitation game: a manual adaptation strategy and an automatic performance-based adaptation strategy. The two strategies were implemented in a competitive game controlled with an inertial-sensor-based home rehabilitation device. They were first evaluated with 32 pairs of unimpaired participants, who played the game with manual adaptation, automated adaptation, or no adaptation. Each variant was played for 9 minutes. Then, the manual and automatic adaptation were also tested by 5 pairs consisting of a person with arm impairment (due to neurological injury) and their unimpaired friend or relative. Throughout the game, motivation was measured with questionnaires while exercise intensity was tracked using the inertial sensors. Results showed that both manual and automatic difficulty adaptation lead to higher motivation and exercise intensity than no adaptation. Unimpaired participants showed no clear preference between manual and automatic adaptation while 4 of 5 impaired participants preferred automatic adaptation. For future use, we propose a combination of manual and automatic adaptation that should be evaluated with more impaired participants in longer multisession experiments.
手动适应策略和基于表现的自动适应策略。这两种策略在基于惯性传感器的家庭康复设备控制的竞争性游戏中得以实现。它们首先在32对未受损参与者中进行评估,这些参与者分别采用手动适应、自动适应或不适应的方式玩游戏。每个变体游戏时长为9分钟。然后,由5对人员进行测试,其中包括一名因神经损伤而手臂受损的人和他们未受损的朋友或亲属,测试手动适应和自动适应情况。在整个游戏过程中,通过问卷测量动机,同时使用惯性传感器跟踪运动强度。结果表明,与不适应相比,手动和自动难度适应都能带来更高的动机和运动强度。未受损参与者在手动适应和自动适应之间没有明显偏好,而5名受损参与者中有4名更喜欢自动适应。为便于未来使用,我们建议将手动适应和自动适应相结合,并应在更长时间的多阶段实验中,对更多受损参与者进行评估。