The rapid increase in the number of young adults living alone gives rise to a demand for the resolution of social isolation problems. Social robot technologies play a substantial role for this purpose. However, existing technologies try to solve the problem only through one-to-one interaction with robots, which in turn fails to utilize the real-world social relationships. Privacy concern is an additional issue since most social robots rely on the visual information for the interactions. To this end, we propose 'Fribo', auditory information centered social robot that recognizes user's activity by analyzing occupants' living noise and shares the activity information with close friends. A four-week field study with the first prototype of Fribo confirms that activity sharing through the use of anonymized living noise promises a virtual cohabiting experience that triggers more frequent real-world social interactions with less feeling of privacy intrusion. Based on this finding and the further qualitative analysis, we suggest a design principle of sound-based social networking robots and its associated new interactions, then present the second prototype of Fribo inspired by the implications from the field study.
|Title of host publication||HRI 2018 - Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2018 Feb 26|
|Event||13th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2018 - Chicago, United States|
Duration: 2018 Mar 5 → 2018 Mar 8
|Name||ACM/IEEE International Conference on Human-Robot Interaction|
|Conference||13th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2018|
|Period||18/3/5 → 18/3/8|
Bibliographical noteFunding Information:
The authors would like to thank Nani Kim for her valuable comments on the paper. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2016R1D1A1B02015987).
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Human-Computer Interaction
- Electrical and Electronic Engineering