Name
Abstract | ||
|---|---|---|
Monitoring health conditions and events of grandparent-headed family is important to increase their quality of life and reduce care burdens. Affective episodes are significant indexes in monitoring behavior changes. In this paper, we propose an information retrieval approach to extract affect words from speech and written text to provide quantitative evidence of physical functions and social interactivity for living support and the health related quality of life assessment. Hidden Markov model with a developed behavior grammar network was adopted to transcribe speech. Combined with written texts, an adjusted term-frequency and a sliding window method were performed to extract and quantify affect words. A quantitative index scored by trigger pair approach was applied to assess affective episodes with time and place. Experimental results and case study revealed that the proposed approach shows encouraging potential in monitoring daily activity and family dialog. Its extension may provide an alternative way to obtain implicit information of emotional expression between a family. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/APSIPA.2014.7041674 | APSIPA |
Keywords | Field | DocType |
medical information systems,term-frequency,affective episodes automatic assessment,information retrieval,health related quality of life assessment,trigger pair approach,grandparent-headed family,health condition monitoring,social interactivity,daily activities analysis,assisted living,behavior grammar network,hidden markov models,sliding window method,hidden markov model,data mining,markov processes,indexes,decision support systems,speech | Interactivity,Activities of daily living,Decision support system,Psychology,Cognitive psychology,Grammar,Emotional expression,Artificial intelligence,Hidden Markov model,Affect (psychology),Machine learning,Behavior change | Conference |
ISSN | Citations | PageRank |
2309-9402 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Hsien Chiu | 1 | 3 | 0.82 |
| Kun-Yi Huang | 2 | 14 | 5.00 |
| Hsiu-E. Chiu | 3 | 0 | 0.34 |
| Wu-ton Chen | 4 | 62 | 10.87 |