Slacking in the context of agent-based assessment in virtual rehabilitation systems

Efforts are already underway to develop technology-derived solutions which automate aspects of conventional therapy. Ideally we would like to develop a human-like virtual therapist, in an attempt to enhance automated rehabilitation particularly in the home setting. One interesting skill of the experienced human therapist that we would like to model is the ability to recognize and manage behavior patterns known to decrease the effectiveness of rehabilitation. A particularly compelling example of such behavior is described in the context of robot-assisted therapy, where it has been demonstrated that “assist-as-needed” strategies may impact negatively on rehabilitation outcomes due to an intrinsic property of the human motor systems that encourages “slacking” as a form of energy optimization. In this work we endeavor to explore and extend this concept by giving it context in the standard therapist-patient interaction setting. We developed an apparatus which can measure and quantify grip strength and an agent based virtual therapist that can assess performance and offer simple natural language feedback in real time. We then conducted a series of experiments with healthy subjects in which the mapping between performance and feedback valence is altered. Our results demonstrate that subject performance is dependent on the feedback rules and that in particular, excessively positive feedback yields performance dynamics analogous to those observed in slacking studies. These preliminary results have implications for the design of virtual therapist systems.