Automatic assessment of affective episodes for daily activities analysis

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.