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|Title:||An Affective Computing Approach to Identify the Potential for Mental Health Problems by Measuring User Affect|
|Issue Date:||14-Sep- 30|
|Abstract:||Emotional Intelligence (EI) is a key indicator of successful and social individuals. Being emotional intelligent they become both intra-personal intelligent, and inter-personal intelligent. They become capable of understanding not only their own feelings and needs, but also of others’. EI is not merely intuitive; it could be enhanced with self-reflections, and by being in company with others. With the proliferation of Computer Mediated Communication (CMC) and digital media, the means of social contact has been drastically altered. It has ensured better social connectedness, but shattered strengths of social ties, marking a downfall of emotional intelligent capabilities of many individuals. There is an inherent after effect: individuals find it difficult to regulate their emotions, seek help from others, and also to provide emotional support to others. The social isolation, and therefore the psychological distress among individuals have been kindled. It may not be possible and worthwhile to bring the pieces back by wiping out CMC and digital media. Instead, it is to be explored how to use this inevitable social change to address the issue positively. Accordingly, it has been investigated how the way the computer users interact with computers can be used to identify their psychological distress. In order to avoid deliberate alterations to the interaction patterns, a non-intrusive mechanism was adopted. The research was conducted using Action Research (AR) method following a progressive development in three consecutive research cycles. During these research cycles carried out, it has been learned that the stress is reflected in the way the users interact with computers; these interactions could be monitored and recorded non-intrusively using an activity logger; and more importantly these measures are actually correlated with the psychological distress. Predicting the prevalence for mental health problems based on the human computer interaction patterns is thereby become viable. Two interesting indicators have been exposed during these efforts. The prediction model does not fit into a linear mathematical equation, nor could make reliable predictions by applying a pre-defined rule set. Instead, the model which gave more promising results used machine learning techniques, and adopted case-based reasoning. This sets out new line of thinking: Is emotional intelligence an ability of case-based learning and reasoning of feelings and needs?|
|Appears in Collections:||2013|
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