A Gamified AI-Driven System for Depression Monitoring and Management
Source abstract
Abstract
Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Mental Health Interventions · Mental Health Research Topics · COVID-19 and Mental Health
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand
Department of Computer Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Department of Data Science and Artificial Intelligence, Auckland University of Technology, Auckland 1010, New Zealand
References (35)
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