Introduction
Homework-based rehabilitation programs can help stroke survivors restore
upper extremity function. However, compensatory motions can develop without
therapist supervision, leading to sub-optimal recovery. We developed a
visual feedback system using a live video feed or an avatar reflecting
users' movements so users are aware of compensations. This pilot study aimed
to evaluate validity (how well the avatar characterizes different types of
compensations) and acceptability of the system.
Methods
Ten participants with chronic stroke performed upper-extremity exercises
under three feedback conditions: none, video, and avatar. Validity was
evaluated by comparing agreement on compensations annotated using video and
avatar images. A usability survey was administered to participants after the
experiment to obtain information on acceptability.
Results
There was substantial agreement between video and avatar images for shoulder
elevation and hip extension (Cohen's κ: 0.6–0.8) and almost perfect
agreement for trunk rotation and flexion (κ: 0.80–1). Acceptability was low
due to lack of corrective prompts and occasional noise with the avatar
display. Most participants suggested that an automatic compensation
detection feature with visual and auditory cuing would improve the
system.
Conclusion
The avatar characterized four types of compensations well. Future work will
involve increasing sensitivity for shoulder elevation and implementing a
method to detect compensations.
In this paper we present the development of an evidence-based search planner for a mobile assistive robot to autonomously search for a dynamic person in a multi-room home environment in order to provide assistance. We solve the dynamic person search problem by uniquely considering evidence of household objects along with a user spatial-temporal model to increase the probability of finding the user. Our planner utilizes a Partially Observable Markov Decision Process (POMDP) to plan optimal robot search paths in the environment as the user and evidence locations are partially observable. Extensive simulated experiments in a home environment were conducted to compare our proposed evidence-based search approach with 1) a search technique without prior user information, and 2) a search technique that only uses a user model. The results show that our proposed search technique has higher success rates for finding the user and is more robust to the dynamic behaviors of the user.
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