Home/Problem Statements/SIH26094

SIH26094SoftwareMedTech / BioTech / HealthTech

₹1,50,000 Prize (₹75k Joint)

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Deadline: 20 September 2026

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Submitted Ideas: 0/500

AI-Powered Dynamic Mental Health Monitoring and Distress Prediction System for Victims of Atrocities

Ministry / Organization: Ministry of Social Justice and Empowerment (MoSJE)

Department: Department of Social Justice and Empowerment

SIH Official Portal

Technical Complexity

8/10

Dataset Accessibility

7/10

36h Hackathon Viability

9/10

Step 0 • Zero-Jargon Layman Summary

“Victims of serious crimes and atrocities suffer severe psychological trauma long after filing a complaint due to lengthy trials, social threats, and financial hardships. We are building an AI-powered monitoring system that regularly checks on victims through calls, chats, and mobile apps, analyses their mental state, predicts crisis risks, and automatically alerts counsellors and authorities before distress turns critical.”

The DecodeX PS Decoder™

Deconstructed Problem Blueprint

Step 1

WHAT TO BUILD?

An integrated multi-channel software platform comprising an AI conversational engine, voice stress analyser, text sentiment processor, dynamic scoring algorithm, automated alert gateway, and hierarchical administrative dashboards.

Step 2

WHO IS IT FOR?

Primary beneficiaries include victims of heinous crimes, caste-based violence, and threatened witnesses under the SC/ST (PoA) Act. Users of the system include victims/complainants (via NHAA 14566, app, IVRS), district social welfare officers, counsellors, district authorities, and national policymakers.

Step 3

WHY DOES IT EXIST?

Current support mechanisms focus entirely on legal paperwork and financial compensation while completely ignoring ongoing mental health. Victims face isolation, intimidation, and trial fatigue, leading to undetected psychological spirals, depression, or self-harm due to a lack of continuous institutional care.

Step 4

INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE

01 • Inputs & Telemetry

Periodic user inputs from voice calls (IVRS/helpline audio), multilingual text chats (WhatsApp/app), interactive survey responses, historical case metadata, and engagement frequency patterns.

02 • Processing & AI Core

Natural Language Processing (NLP) for text sentiment, Emotion AI and acoustic feature extraction for voice stress analysis, longitudinal trend calculation to generate a Dynamic Distress Score, and rule-based threshold evaluation for automated risk escalation.

03 • Actionable Deliverables

Real-time automated risk alerts sent to counsellors/authorities via SMS/dashboards, recommended intervention pathways (legal aid, medical counselling, witness protection), and state/national level visual analytics dashboards.

Step 5

CONSTRAINTS

Must support regional Indian languages and dialects with high accuracy.
Strict compliance with data privacy, victim confidentiality, and anonymization mandates.
Must operate reliably across low-bandwidth or intermittent network connectivity zones (offline-first sync for field workers).
Explainable AI requirements so authorities understand why a risk score spiked.

Step 6

JURY METRICS

Voice stress and sentiment classification accuracy (Precision/Recall > 85%).
Alert latency (time taken from distress detection to counsellor notification < 5 seconds).
User interface simplicity and accessibility score for semi-literate or distressed users.
Reduction in false positive alert rates for high-risk triage.

Step 7

CORE BOTTLENECK

Accurately capturing and normalising emotional state from noisy, low-quality telephony or IVRS audio recordings across diverse regional Indian accents without violating user trust or breaching data privacy.

Why You Should Choose This PS (Pros)

Massive social impact alignment with the Ministry of Social Justice and Empowerment (MoSJE).
Clear, highly structured workflows and well-defined output dashboards.
Strong 'wow factor' through multimodal AI (Emotion AI + Voice Stress Analytics + Predictive Scoring).
Direct applicability to national helplines like NHAA 14566.

Challenges & Risks to Watch (Cons)

Handling sensitive mental health and trauma data requires meticulous ethical and security frameworks.
Synthesising realistic dummy audio datasets for training and live demonstration can be challenging.
Complex integration requirements if connecting to legacy government telecom/IVRS infrastructure.

Hackathon Blueprint

Recommended Tech Stack & Grand Finale Features

Frontend & Mobile

React.jsTailwind CSSChart.js / Recharts for longitudinal trends

Backend & APIs

FastAPI (Python)Celery for background async task processingWebSockets for real-time dashboard alerts

AI / ML & Models

OpenAI Whisper / IndicWhisper for multilingual speech-to-textHuggingFace transformers (RoBERTa / IndicBERT) for sentiment & emotion analysisScikit-learn / XGBoost for predictive risk scoring models

Database & Cloud

PostgreSQL with encryption-at-restRedis for real-time session and alert cachingMinIO for secure encrypted audio/document storage

Killer Features to Impress the SIH Jury

Acoustic Voice Stress Profiler that detects micro-tremors and pitch variations indicative of severe trauma or panic from raw audio.

Explainable Risk Cards that break down exactly which text keywords or voice markers caused a spike in the Dynamic Distress Score.

One-Click Secure Intervention Dispatcher that instantly routes case files and geo-mapped resources to local district counsellors.

Anonymised Multi-Tiered Dashboards with drill-down views from National to District levels for evidence-based policymaking.

View Official SIH 2026 Portal Raw Text

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•Background Victims of atrocities frequently experience prolonged psychological distress after complaint registration due to threats, intimidation, repeated court appearances, delays in investigation and trial, social ostracism, economic hardship, and rehabilitation challenges.Existing mechanisms focus primarily on legal and financial support and do not provide continuous monitoring of victim well-being.

•Problem Statement Develop an AI-based Dynamic Mental Health Monitoring and Distress Prediction System that continuously monitors and predicts psychological distress among victims and complainants registered through NHAA (14566), the Integrated Portal, chatbot, mobile application, IVRS, or other approved communication channels throughout the investigation,trial, rehabilitation, and compensation process.

•Expected Solution The system should:

•Conduct periodic interactions with victims through chatbot, IVRS calls, SMS, mobile applications, web portal, or helpline follow-up mechanisms.

•Analyse voice, text, behavioural responses, and engagement patterns using NLP, Sentiment Analysis, and Emotion AI.

•Generate a Dynamic Distress Score and longitudinal trend analysis.

•Predict escalation of psychological distress before a crisis situation emerges.

•Trigger alerts to counsellors, district authorities, and designated officials when predefined risk thresholds are crossed.

•Recommend appropriate interventions such as counselling, medical treatment, witness protection, relocation support, financial assistance, legal aid, or rehabilitation measures.

•Provide dashboards at district, State, and national levels for monitoring vulnerable victims and high-risk cases.

•Ensure explainable AI, privacy protection, data security, and compliance with applicable legal and ethical standards.

•Expected Outcomes

•Continuous monitoring of victim well-being.

•Early detection and prevention of mental health crises.

•Timely deployment of counselling and rehabilitation services.

•Strengthened victim confidence in the justice delivery system.

•Evidence-based decision-making for policymakers and administrators.

•Improved coordination among welfare, counselling, and law-enforcement agencies.

•Innovation Components

•Emotion AI

•Voice Stress Analytics

•Sentiment Analysis

•Predictive Risk Modelling

•Multilingual Conversational AI

•Explainable AI

•Automated Case Prioritisation

•Real-Time Risk Alerts

•Priority Use Cases

•Victims of rape and gang rape.

•Victims of murder, grievous hurt, and arson.

•Witnesses facing intimidation or threats.

•Families affected by caste-based violence.

Beneficiaries receiving relief, compensation, rehabilitation, and protection under the provisions of the Scheduled Castes and Scheduled Tribes (Prevention of Atrocities) Act, 1989.