Increasing the efficacy of rehabilitation protocols for children via a robotic playmate providing real-time corrective feedback

2016 
Previous studies have shown that external corrective feedback provided to individuals undergoing physical therapy sessions increases the efficacy of their intervention protocols, thus allowing for the individual to rapidly improve on their performance. However, direct feedback is typically provided by an expert therapist during weekly or monthly visits, which limits improvement on a daily basis. As such, we promote in-home rehabilitation protocols by developing a novel framework that couples serious games with a robotic playmate. After showing in a previous study that a combination of verbal and nonverbal cues is the most efficient method for providing guided instruction, we now show that the efficacy of the intervention protocols can be further increased by embedding human-like behaviors on the robotic platform such that it provides continuous feedback. This would allow user performance to improve at each iteration of feedback and, ultimately, reach an individualized performance goal. To show that users can reach a reference point for a given kinematic parameter, we recruited adults and children to interact with our system. Results from the first round of experiments show that adults who received feedback from an embodied agent need less trials on average to reach their performance goals than the adults who received feedback from a virtual agent. Results from the second round of experiments further support our hypothesis by showing that children also reach their performance goals by receiving feedback from the robotic playmate. We conclude that coupling existing serious games with an embodied robotic agent has the potential of increasing the efficacy of intervention protocols by allowing for use outside the clinical setting thus increasing users' rate of improvement.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    19
    References
    4
    Citations
    NaN
    KQI
    []