Revolutionizing Mental Health Support: An Innovative Affective Mobile Framework for Dynamic, Proactive, and Context-Adaptive Conversational Agents

TL;DR

This paper proposes an innovative affective mobile framework for mental health support, integrating real-time emotional sensing, contextual awareness, and therapeutic tools to create empathetic, proactive conversational agents for personalized mental health interventions.

Contribution

It introduces a novel framework combining facial expression analysis, physiological signals, and language models with therapeutic techniques to enhance chatbot emotional understanding for mental health support.

Findings

Integrates facial affect detection with passive sensing for emotion recognition.

Employs cognitive-behavioral therapy tools within chatbot interactions.

Proposes a system to improve emotional engagement and mood management.

Abstract

As we build towards developing interactive systems that can recognize human emotional states and respond to individual needs more intuitively and empathetically in more personalized and context-aware computing time. This is especially important regarding mental health support, with a rising need for immediate, non-intrusive help tailored to each individual. Individual mental health and the complex nature of human emotions call for novel approaches beyond conventional proactive and reactive-based chatbot approaches. In this position paper, we will explore how to create Chatbots that can sense, interpret, and intervene in emotional signals by combining real-time facial expression analysis, physiological signal interpretation, and language models. This is achieved by incorporating facial affect detection into existing practical and ubiquitous passive sensing contexts, thus empowering them with the capabilities to the ubiquity of sensing behavioral primitives to recognize, interpret, and respond to human emotions. In parallel, the system employs cognitive-behavioral therapy tools such as cognitive reframing and mood journals, leveraging the therapeutic intervention potential of Chatbots in mental health contexts. Finally, we propose a project to build a system that enhances the emotional understanding of Chatbots to engage users in chat-based intervention, thereby helping manage their mood.