Mental Health and Well-Being Surveillance, Assessment, and Tracking Solution Among Children

Abstract

Children\'s mental health has become an increasingly critical public health concern, necessitating advanced surveillance, assessment, and tracking solutions. The rapid rise in mental health issues among adolescents highlights the need for technology-driven interventions that facilitate early detection and intervention. The research explores an innovative Android-based solution designed to monitor and track children\'s mental well-being through interactive assessments, behavioural tracking, and machine learning algorithms. The proposed system provides real-time analysis, personalized interventions, and privacy-protected data management to ensure proactive mental health support. By integrating digital tools with psychological expertise, this study emphasizes the potential of AI-based applications in enhancing children\'s mental health outcomes. The research contributes to ongoing efforts in improving accessibility, reducing stigma, and increasing early intervention opportunities in paediatric mental healthcare

Introduction

Introduction

Adolescence is a pivotal stage of psychological and emotional development, increasingly affected by factors like academic pressure, peer influence, family issues, and digital media. Globally, 1 in 7 adolescents suffers from mental health conditions, yet many cases go undiagnosed and untreated. There is a growing need for technology-driven, accessible, and scalable solutions to assess and monitor children's mental health.

This research introduces an AI-powered Android application that uses gamified assessments, real-time behavioral tracking, and machine learning analysis to evaluate and support children’s mental well-being.

2. Literature Review

Key contributions in the field include:

• Achenbach & Rescorla (2001): Standardized emotional and behavioral assessment tools.

• Boyd et al. (2018): Identified mental health service gaps.

• Fazel et al. (2014): Advocated for school-based mental health interventions.

• Kim-Cohen et al. (2003): Emphasized early detection through longitudinal research.

• Skokauskas & Fung (2019): Stressed global pediatric mental health service challenges.

Studies show digital tools like mobile apps and AI improve accessibility, early detection, and engagement, helping address existing limitations in traditional mental health assessments.

3. Methodology

The application is developed using Python, Flask, SQLite3 and features:

• User Registration: Parents, teachers, or healthcare providers register and input child data.

• Interactive & Gamified Assessments: Children participate in engaging tasks that measure cognitive and emotional responses.

• Passive Monitoring: Tracks behavioral patterns like reaction time and engagement.

• AI Analysis: Machine learning models evaluate the collected data to detect early signs of conditions like anxiety or depression.

• Personalized Interventions: Risk levels are assessed, and specific recommendations are generated.

• User Interface: Child-friendly, multilingual, accessible for users with cognitive differences.

• Security & Compliance: Strong data encryption and adherence to privacy laws (GDPR, COPPA).

4. System Design

• Frontend & Backend: Built using React Native, Java, XML on platforms like Android Studio, VS Code.

• Database: MySQL, SQLite.

• Hardware Requirements: Minimum Intel i3 or AMD Ryzen 5, 8 GB RAM, 500 GB HDD or 250 GB SSD.

5. Results

Initial pilot testing showed:

• High engagement due to gamification.

• Accurate detection of emotional states.

• Timely alerts enabled faster intervention by caregivers.

• Secure, ethical data handling and compliance with privacy regulations.

Conclusion

The integration of artificial intelligence and mobile technology in children\'s mental health assessment offers a scalable and innovative approach to early diagnosis and intervention. By leveraging AI-driven behavioural analysis, gamification, and real-time data tracking, this research highlights the potential of digital solutions in empowering parents, educators, and healthcare professionals to identify mental health concerns early. This system bridges accessibility gaps, making mental health support more inclusive and engaging for children while ensuring accurate and non-intrusive assessments. A key strength of this project lies in its emphasis on data security, privacy, and ethical considerations. Robust encryption, authentication protocols, and access control measures ensure user confidentiality and promote long-term trust in the system. However, further refinements are necessary, including expanding AI training datasets for better accuracy across diverse populations and integrating telehealth services to facilitate direct intervention by mental health professionals. Collaborations with psychologists and behavioural scientists will also enhance the effectiveness and reliability of the system’s recommendations. In conclusion, this research establishes a strong foundation for the future of AI-driven mental health assessments. As technology evolves, integrating predictive analytics, biometric analysis, and cross-platform compatibility with schools and healthcare institutions will enhance the system’s impact. By continuously refining and expanding these capabilities, this approach has the potential to transform mental health monitoring, ensuring that every child receives the necessary support to thrive emotionally and psychologically.

References

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Copyright

Copyright © 2025 Pranay C. Pinjarkar, Kartik A. Sonwane, Kartik V. Shinde, Prof. Dipali A. Sananse. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.