Abstract
Leisure-time physical activity (LTPA) has been shown to be an effective way of preventing diseases. However 50% of the people who start any type of sports or LTPA drop out of the program within 6 months. Even though a lot of research on exercise adherence has been performed, no common consensus on all the leading factors to adherence exists. Cultural differences make studies in other countries not suitable for Japan. In this paper we present designs of recommender systems to recommend LTPA to adults. Due to complexities of problem domain and vast number of possible LTPA, first we assume people with similar lifestyle may follow each other better than others as far as exercise is concerned. Based on lifestyle data in complete health check-ups (Ningendoku) that is performed in Japan, we find similar people. In addition, based on expertise and detailed information of different sports and activities we build ontology trees and tables for different attributes of LTPA, these ontologies are used to calculate distances of different LTPA. Health-care professionals in preliminary discussion showed a lot of interest on our system.
| Original language | English |
|---|---|
| Title of host publication | MCCSIS'08 - IADIS Multi Conference on Computer Science and Information Systems; Proceedings of Informatics 2008 and Data Mining 2008 |
| Pages | 148-152 |
| Number of pages | 5 |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | Informatics 2008 and Data Mining 2008, MCCSIS'08 - IADIS Multi Conference on Computer Science and Information Systems - Amsterdam Duration: 2008 Jul 22 → 2008 Jul 27 |
Other
| Other | Informatics 2008 and Data Mining 2008, MCCSIS'08 - IADIS Multi Conference on Computer Science and Information Systems |
|---|---|
| City | Amsterdam |
| Period | 08/7/22 → 08/7/27 |
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ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Software
Cite this
Design of physical activity recommendation system. / Sami, Ashkan; Nagatomi, Ryoichi; Terabe, Masahiro; Hashimoto, Kazuo.
MCCSIS'08 - IADIS Multi Conference on Computer Science and Information Systems; Proceedings of Informatics 2008 and Data Mining 2008. 2008. p. 148-152.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Design of physical activity recommendation system
AU - Sami, Ashkan
AU - Nagatomi, Ryoichi
AU - Terabe, Masahiro
AU - Hashimoto, Kazuo
PY - 2008
Y1 - 2008
N2 - Leisure-time physical activity (LTPA) has been shown to be an effective way of preventing diseases. However 50% of the people who start any type of sports or LTPA drop out of the program within 6 months. Even though a lot of research on exercise adherence has been performed, no common consensus on all the leading factors to adherence exists. Cultural differences make studies in other countries not suitable for Japan. In this paper we present designs of recommender systems to recommend LTPA to adults. Due to complexities of problem domain and vast number of possible LTPA, first we assume people with similar lifestyle may follow each other better than others as far as exercise is concerned. Based on lifestyle data in complete health check-ups (Ningendoku) that is performed in Japan, we find similar people. In addition, based on expertise and detailed information of different sports and activities we build ontology trees and tables for different attributes of LTPA, these ontologies are used to calculate distances of different LTPA. Health-care professionals in preliminary discussion showed a lot of interest on our system.
AB - Leisure-time physical activity (LTPA) has been shown to be an effective way of preventing diseases. However 50% of the people who start any type of sports or LTPA drop out of the program within 6 months. Even though a lot of research on exercise adherence has been performed, no common consensus on all the leading factors to adherence exists. Cultural differences make studies in other countries not suitable for Japan. In this paper we present designs of recommender systems to recommend LTPA to adults. Due to complexities of problem domain and vast number of possible LTPA, first we assume people with similar lifestyle may follow each other better than others as far as exercise is concerned. Based on lifestyle data in complete health check-ups (Ningendoku) that is performed in Japan, we find similar people. In addition, based on expertise and detailed information of different sports and activities we build ontology trees and tables for different attributes of LTPA, these ontologies are used to calculate distances of different LTPA. Health-care professionals in preliminary discussion showed a lot of interest on our system.
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M3 - Conference contribution
SN - 9789728924638
SP - 148
EP - 152
BT - MCCSIS'08 - IADIS Multi Conference on Computer Science and Information Systems; Proceedings of Informatics 2008 and Data Mining 2008
ER -