This paper presents an elder care robot system for older people. An elder care robot system provides human-robot interaction application and monitoring service. The study was carried out for five weeks in nursing homes, with the participation of 17 residents. We have conducted a t-test to compare the difference between satisfaction and usability evaluation for the residents and the staff.
Purpose: The purpose of this study was to identify the effects on cognitive function, depression, and activities of daily living of SilverCare-Robot Program for institutionalized elders. Methods: This study was a nonequivalent control group pretest-posttest design. The participants were 42 institutionalized elders (17 in the experimental group and 25 in the control group). The Silver-Care-Robot Program was provided as an intervention which was conducted twice a week for 5 weeks. The Silver-Care-Robot Program is an integrated entertainment program to help the mental, emotional, and physical health of elderly people. Pre-test was conducted on the two groups, and, in order to examine the effects of intervention, a post-test was conducted after 5 weeks. Results: There were significant differences in cognitive function and ADL (activities of daily living) between two groups after the program. But the difference in depression in the institutionalized elders was not statistically significant between the two groups. Conclusion: The Silver-Care-Robot Program should be considered as a regular program for cognitive function and activities of daily living for institutionalized elders. If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.
This paper presents a process to build a classifier in a datadriven way for recognizing engagement of children in a robotbased math quiz game. The process consists of collecting video recordings from HRI experiments; annotating the social signals and engagement states via video analysis; extracting feature vectors from the annotations and training classifiers. We conducted an experiment with 7 participants of 10 ∼ 11 years of age using an android robot EveR-4. With three coders annotating the video recordings and extracting features by snapshot model with 1-second time window, we achieved 84.83% recall performance.
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