Welcome to the InfoSci Platform
Reference Hub1
Research on Key Technology in Remote Education System of Spirit Diagnosing by Eye in TCM

Research on Key Technology in Remote Education System of Spirit Diagnosing by Eye in TCM

Feng Guo, Shaozi Li, Ying Dai, Changle Zhou, Ying Lin
Copyright: © 2011 |Volume: 9 |Issue: 1 |Pages: 13
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781613506578|DOI: 10.4018/jdet.2011010107
Cite Article Cite Article

MLA

Guo, Feng, et al. "Research on Key Technology in Remote Education System of Spirit Diagnosing by Eye in TCM." IJDET vol.9, no.1 2011: pp.101-113. http://doi.org/10.4018/jdet.2011010107

APA

Guo, F., Li, S., Dai, Y., Zhou, C., & Lin, Y. (2011). Research on Key Technology in Remote Education System of Spirit Diagnosing by Eye in TCM. International Journal of Distance Education Technologies (IJDET), 9(1), 101-113. http://doi.org/10.4018/jdet.2011010107

Chicago

Guo, Feng, et al. "Research on Key Technology in Remote Education System of Spirit Diagnosing by Eye in TCM," International Journal of Distance Education Technologies (IJDET) 9, no.1: 101-113. http://doi.org/10.4018/jdet.2011010107

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Spirit diagnosing is an important theory in TCM (Traditional Chinese Medicine), by which a TCM doctor can diagnose a patient’s body state. But this theory is complicated and difficult to master simply learned from books. To further the theory and skill of spirit diagnosing, in this paper, the authors propose a remote education system that can accept videos from a user and give the user an auto-diagnosed spirit. The key technology in this system is eye feature computation in spirit diagnosing, for which rules describing “the spirit” (spirit in TCM refers to the human’s mental state which reflects the one’s general physical condition) state are mined by the quantitative features regarding the human eyes. With videos capturing eye condition during a short period, a set of eye features are extracted. On this basis, attribute intervals of the eye feature space is generated by CAIM (class-attribute interdependence maximization). Several of the candidate rules are then mined by the association rule based on the cloud model. Finally, three complementary rule-pruning methods are modified and combined to trim the candidate rules. The cross validation test for mined rules has an average accuracy of 93%, which shows the high performance of the proposed method.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.