Haruhisa Maeda
ABSTRACT
To support in-home long-term care, we are studying techniques of Mind Sensing, which externalizes internal states of elderly people as words through conversations with agents or robots. We have previously developed a prototype system of Mind Sensing, integrated with an activity recognition system and an LINE chatbot. However, the system was tightly coupled with the fixed systems, it was difficult to add or change the setting of questions from the chatbot to individual elderly people.
In this paper, we propose the Mind Sensing Service, which allows a service operator to define and manage the questions flexibly, and to automate the delivery of the questions and the collection of the answers. The proposed service consist of two elements: actions and rules. An action defines the contents of specific questions such as what message is sent to which elderly people. A rule defines the conditions on when, where, and by what event, in order to execute the action. The proposed service makes it possible to implement more systematic and flexible Mind Sensing.
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- Haruhisa Maeda, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda. 2019. Memory Aid Service Using Mind Sensing and Daily Retrospective by Virtual Agent. In 10th International Conference, DHM 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Vol. LNCS 11582. Springer, 353--364.Google ScholarCross Ref
- Masahide Nakamura, Kenji Hatano, Jun Miyazaki, Kiyoshi Yasuda, Noriaki Kuwahara, Hiroaki Kazui, Sachio Saiki, Seiki Tokunaga, Mihoko Otake, Naoki Kodama, and Naoko Kosugi. 2019. Developing a System for Self-care and Mutual-aids of Elderly People at Home Based on Externalization of Internal States. JSPS KAKENHI, Grant-in-Aid for Scientific Research (A) 19H01138 (2019).Google Scholar
- Shota Nakatani, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda. 2018. Generating Personalized Virtual Agent in Speech Dialogue System for People with Dementia. In Digital Human Modeling 2018 (DHM 2018), Held as Part of HCI International 2018, Vol. LNCS 10917. Springer, 326--337. Las Vegas, USA.Google Scholar
- Long Niu, Sachio Saiki, and Masahide Nakamura. 2018. Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household. International Journal of Software Innovation (IJSI) 6, 4 (August 2018), 16--29.Google Scholar
- Kazunari Tamamizu, Seiji Sakakibara, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda. 2018. Machine Learning Approach to Recognizing Indoor Activities based on Detection of Envi-ronmental Change. In 11th World conference of Gerontechnology (ISG2018), Vol. 17. 118s. St. Petersburg, USA.Google Scholar
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- Zhihua Wang, Zhaochu Yang, and Tao Dong. 2017. A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time. Sensors 17, 2 (2017). https://doi.org/10.3390/s17020341Google Scholar
Index Terms
- Rule-Based Inquiry Service to Elderly at Home for Efficient Mind Sensing
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