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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Feng Guo Ying Dai Shaozi Li Ito, K. |
| Copyright Year | 2010 |
| Description | Author affiliation: Fujian Key Lab of the Brain-like Intelligent Systems, Xiamen University, China (Feng Guo) || Cognitive Science department, Xiamen University, China (Shaozi Li) || Faculty of Software and Info. Science, Iwate Pref. University, China (Ying Dai; Ito, K.) |
| Abstract | This paper represents an approach automatically inferring users' sub-healthy state and the corresponding Traditional Chinese Medicine (TCM) syndrome based on TCM doctors' knowledge, so as to construct a system which helps users recuperating from the sub-health by means of showing them the relevant regimen. The system's framework involves utilizing objects' facial features, feelings, and physical status to infer their sub-healthy state and TCM syndrome, and providing the relevant mental care plan and diet plan according to their TCM syndrome. This paper also introduces the data collection for training the inference model, and describes the algorithm of association rule mining while the individuality of TCM doctor's criteria in diagnosing is considered. Experiment results demonstrate the effectiveness and efficiency of the proposed method, and show the potential important role of the TCM in diagnosing people's sub-healthy state, and helping them to recuperate from it. |
| Starting Page | 3717 |
| Ending Page | 3724 |
| File Size | 815957 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424465866 |
| ISSN | 1062922X |
| e-ISBN | 9781424465880 |
| DOI | 10.1109/ICSMC.2010.5641849 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-10 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Heating Medical services individual difference Sub-health TCM Syndrome Auto-Diagnosis Rule Integration |
| Content Type | Text |
| Resource Type | Article |
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