Biblio

The number of people suffering from mental health issues like depression and anxiety have spiked enormously in recent times. Conversational agents like chatbots have emerged as an effective way for users to express their feelings and anxious thoughts and in turn obtain some empathetic reply that would relieve their anxiety. In our work, we construct two types of empathetic conversational agent models based on sequence-to-sequence modeling with and without attention mechanism. We implement the attention mechanism proposed by Bahdanau et al. for neural machine translation models. We train our model on the benchmark Facebook Empathetic Dialogue dataset and the BLEU scores are computed. Our empathetic conversational agent model incorporating attention mechanism generates better quality empathetic responses and is better in capturing human feelings and emotions in the conversation.

Purpose: Smart apps and wearables devices are an increasingly used way in healthcare to monitor a range of functions associated with certain health conditions. Even if in the present there are some devices and applications developed, there is no sufficient evidence of the use of such wearables devices in the detection of some disorders such as depression. Thus, through this paper, we want to address this need and present a quantitative research to determine the user requirements for developing a smart device that can detect depression. Material and Methods: To determine the user requirements for developing a system to detect depression we developed a questionnaire which was applied to 205 participants. Results and conclusions: Such a system addressed to detect depression is of interest among the respondents. The most essential parameters to be monitored refer to sleep quality, level of stress, circadian rhythm, and heart rate. Also, the developed system should prioritize reliability, privacy, security, and ease of use.