Field of the Invention
The present invention relates to the Child Abuse Mental Symptom Prediction Mo~el (CAMSPM),
an ·AI-driven system designed to predict and analyze mental health symptoms in children who
have experienced abuse. By leveraging machine learning techniques, facial expression analysis,
voice recognition, and handwriting detection, CAMSPM offers a comprehensive and data-driven
approach to identifying early signs of psychological distress and facilitating timely interventions.
This invention specifically addresses key aspects of child abuse detection, including:
• Survey-Based Analysis: Collecting and analyzing demographic, behavioral, and socioeconomic
· factors to assess potential mental health risks.
• Facial Expression Recognition: Utilizing Al-powered facial analysis to detect emotional
distress markers such as eyel)row positioning, eye openness, and mouth shape.
• Voice Emotion Recognition: Examining speech patterns, tone, and intensity to identify
signs of anxiety, fear, or sadness.
• Handwriting Analysis: Assessing writing pressure, slant, and structure for indications of
emotional instability or distress.
Unlike traditional psychological assessments that require extensive human evaluation,
CAMSPM integrates real-time Al-driven analysis to provide accurate .and immediate
insights, ensuring early intervention and support for affected children.
2 .Background of the Invention Child abuse is a significant societal issue that profoundly
impacts a child's mental and emotional well-being. Many victims do not receive timely
psychological assessments due to stigma, lack of awareness, and limited access to mental
health professionals. The need for an Al-driven solution capable of detecting early
symptoms of psychological distress has become increasingly critical.
Challenges in Women's Healthcare Accessibility and Personalized Guidance:
I. Limited Access to Psychological Assessments:
o Many children lack access to professional mental health evaluations, delaying
necessary intervention
o Rural and underprivileged areas often have insufficient mental health facilities,
making proper assessment challenging.
2. Underreporting Due to Fear and Stigma:
o Children may hesitate to disclose abuse due to ·rear, shame, or social pressure.
o Signs of distress may be misinterpreted or overlooked in traditional settings
3. Inconsistent Monitoring of Mental Health Symptoms:
o Behavioral changes in children often go unnoticed, leading ·to delayed recognition
of psychological trauma.
o Traditional assessment methods rely heavily on human observation, which may
lack consistency and objectivity.
The Need for AI-Driven Solutions to Provide Real-Time Support:
• AI Juuclels can process large-scale data, analyze multiple parameters, and detect patterns
indicative of mental health issues.
• Machine learning-based emotion detection systems provide real-time insights, enabling
quicker intervention and therapy.
• Voice, handwriting, and facial analysis improve detection accuracy, reducing reliance on
self-reported symptoms.
Limitations of Existing Health Apps:
• Many traditional methods depend on subjective interpretation and require extensive time
for evaluation.
• Standardized tests may not capture real-time distress indicators in children.
• Current psychological tools lack integration with Al-driven multi-modal detection
systems .
To bridge these gaps, CAMSPM has been developed as a real-time Al-powered model that
integrates machine learning, emotion recognition, and behavioral analysis to detect early signs of
psychological distress in children affected by abuse.
Summary of the Invention
CAMS PM is an Al-drive·n child abuse mental symptom prediction model designed to analyze and
predict potential mental health risks in abused children. By integrating machine learning, facial
expression recognition, voice emotion detection, and handwriting analysis, CAMSPM offers a
multi-modal approach to identifying signs of psychological distress.

Key Features:
• Survey-Based Predictive Analysis: Utilizes demographic, behavioral, and socio-.
economic data to assess mental health risks.
• Facial Expression Recognition: Al-powered analysis of micro-expressions to detect
emotional distress.
• Voice Emotion Recognition: Identifies anxiety, fear, and sadness through tone and
speech analysis.
• Handwriting Analysis: Evaluates writing patterns and pressure to assess emotional
instability.
Serene processes both text and voice inputs, offering instant responses, proactive reminders, and
personalized recommendations. Unlike static health apps, it adapts to individual user needs,
ensuring empathetic and accessible health guidance.
Detailed Description of the Invention
Technology Stack:
CAMSPM is built using Python and leverages machine learning, deep learning, and Al-based
emotional recognition algorithms for predictive analysis. The system is trained on a dataset
comprising clinical records, psychological assessments, and social-demographic data.
Interaction Modes:
• Survey-Based Input: Collects demographic, behavioral, and social-economic data for
analysis.
• Facial Expression Analysis: Uses AI models (e.g., Random Forest) to examine facial
features for distress indicators.
• Voice Recognition: Detects emotional states through tone, pitch, and modulation.
• Handwriting Analysis: Identifies psychological distress marker~ in writing style and
pressure.
Core Features:
I. Predictive Mental Health Risk Assessment:
o Analyzes multi-source data to determine the probability of anxiety, PTSD, or
depression.

o Provides detailed reports with severity classifications.
2. Facial Emotion Recognition:
o Detects micro-expressions associated with distress, fear, or sadness.
o Uses a trained model to classify emotional states.
3. Voice-Based Emotional Detection:
o Identifies vocal stress and instability.
o Differentiates between normal speech and anxiety-driven voice patterns.
4. Handwriting-Based Analysis:
o Evaluates consistency, pressure, and structure for psychological insights.
o Flags potential distress indicators.
Personalization:
o Learns from child-specific data to improve accuracy over time.
o Offers tailored intervention strategies based on real-time assessments.
o Provides actionable recommendations for therapists and caregivers ..
Advantages of the Invention
Early Detection and Prevention: Helps identify mental health risks before symptoms
escalate.
• At-Powered Multi-Modal Analysis: Combines survey, facial recognition, voice, and
handwriting inputs for comprehensive assessment.
• Objective and Data-Driven Insights: Reduces subjective bias and enhances detection
accuracy.
• Timely Mental Health Interventions: Facilitates faster access to psychological support
services.
• Privacy-Preserving and Secure: Ensures confidential processing of sensitive dat

CLAIMS
1. AI-Driven Mental Health Prediction System - A multi-modal system that
predicts child abuse-related mental health risks using machine learning.
2. Facial Expression Analysis for Psychological Assessment - At-powered
detection of emotional distress in children.
3. Voice Emotion Recognition for Abuse Detection - Identifies anxiety and fear
through vocal patterns.
4. Handwriting-Based Mental Health Assessment - Evaluates psychological
distress markers from writing samples.
5. Automated Mental Health Risk Alerts - Notifies caregivers and professionals
about high-risk cases.
6. Personalized AI-Based Mental Health Insights - Provides customized
recommendations based on real-time data.
ADVANTAGES OF THE INVENTION
1. Proactive Health Management
• Offers reminders for self-exams, cycle tracking, and pregnancy milestones.
• Alerts users about potential health concerns based on reported symptoms.
2. Seamless Integration with Digital Health Ecosystems
• Can be incorporated into existing healthcare applications or Learning Management
Systems (LMS) for medical education.
• Provides easy access to health tracking, reports, and personalized insights.··
3. Time-Efficient and Scalable
• Automates health guidance, reducing the need for constant manual research.
• Scales easily to support a diverse user base with multilingual capabilities.
4. Data-Driven Insights for Continuous Improvement
• Allearns
CONCLUSION
The Child Abuse Mental Symptom Prediction Model (CAMSPM) represents a transformative
advancement in child welfare and mental health technology. By integrating machine learning,
facial expression recognition, voice analysis, and handwriting detection, CAMSPM provides an
accurate, real-time assessment of mental health risks in abused children. This system enables
early intervention, ensuring timely support and care for affected individuals.
Future improvements will focus on expanding datasets, refining detection accuracy, and
incorporating real-time learning algorithms to enhance predictive capabilities and intervention
effectiveness.