Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 14, 2022
Open Peer Review Period: Jul 14, 2022 - Sep 8, 2022
Date Accepted: Jul 11, 2024
(closed for review but you can still tweet)
Investigating Older Adult Usage of Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis study
ABSTRACT
Background:
Geriatric care costs and the shortage of workers are becoming a major global concern. Socially Assistive Robots (SARs) have the potential to address these issues, however developing SARs for various types of users is still in its infancy.
Objective:
This study aims to examine the characteristics and usage patterns of SARs.
Methods:
We first quantitatively examine the usage patterns via time-series clustering and time-series analysis using longitudinal log data of a SAR called Hyodol. We then characterize different types of user clusters using user profiling.
Results:
Overall, four time-series clusters are created: (Helpers, Friends, Short-term User, Long-term Users) from two functions, interactive relationship and robot-assisted contents of Hyodol. To further understand these usage patterns, we conducted user profiling, and found users’ usage of robot-assisted content correlated with demographic factors, the surrounding environments of older adults, and their specific psychological situations.
Conclusions:
This study extends our understanding on the factors associated with long-term usage of SARs for geriatric care and makes methodological contributions.
Citation
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Copyright
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