SIH26186/MedTech / BioTech / HealthTech

Uniformed personnel stress and welfare platform

A software platform that uses artificial intelligence and HR data to predict stress levels in uniformed personnel, providing a mobile app for self-assessment and a dashboard for commanders.

Ministry of Home Affairs · Central Reserve Police Force (CRPF), Police II Division · Software

Teams already in: 1/500almost everyone who enters · read 1 hour ago
Buildable in 36 hours: ModerateOur estimate, from their wording
Hard blockers: NoneNothing you must source first
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in Dataset

Whether this suits your team

Why this fits you

This organisation declared winners on 10 problem statement(s) in SIH 2025. It has awarded 4 joint winner(s), so it sometimes splits a prize between two teams.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Wellness Monitoring Dashboard
A web dashboard for commanders and welfare officers to view stress risk assessments and welfare recommendations.
02
Mobile Assessment App
A mobile application that allows personnel to complete voluntary wellness assessments and self-reporting.
03
Predictive Analytics Engine
An AI engine that analyzes HR metrics, deployment history, and behavioral patterns to detect stress risks.
04
Intervention Recommendation System
A module that suggests proactive counseling, workload balancing, and welfare measures.
05
Privacy and Security Framework
A system implementing role-based access control, data anonymization, and secure storage.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The jury will test how well the system protects user privacy, enforces role-based access control, and handles sensitive psychological data securely without leading to disciplinary stigmatization.

They never say

specific machine learning algorithms or frameworks to use
exact HRMS database schemas for integration
the specific clinical scales or questionnaires required for self-assessment

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Specific machine learning algorithms or frameworks to use?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact HRMS database schemas for integration?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
The specific clinical scales or questionnaires required for self-assessment?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

9 more— the ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: Provided — open it
Does the brief need data?: Yes, and they gave it to you.

Also from Ministry of Home Affairs

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26186, set by Ministry of Home Affairs.
Its official title, word for word: "AI-Based Predictive Personnel Stress and Welfare Monitoring System for Uniformed Forces"
Theme: MedTech / BioTech / HealthTech. Category: software.

In plain terms: A software platform that uses artificial intelligence and HR data to predict stress levels in uniformed personnel, providing a mobile app for self-assessment and a dashboard for commanders.

## The organisers' full description, verbatim

• Background Personnel serving in Central Armed Police Forces (CAPFs), Armed Forces, and other uniformed services operate under physically demanding, psychologically stressful, and often hazardous conditions.Extended deployments, operational pressures, separation from families,irregular working hours, and exposure to traumatic incidents can significantly impact mental well-being.Currently, stress identification largely depends on manual observation and self-reporting, which may delay timely intervention. There is a need for a proactive, technology-driven solution that can identify early indicators of stress, burnout, and psychological distress while maintaining privacy and organizational trust. • Description The proposed solution aims to develop an AI-powered Personnel Stress and Welfare Monitoring System capable of identifying potential indicators of stress, burnout, emotional fatigue, and welfare concerns through analysis of organizational and voluntarily provided wellness data.The system should: • Analyze HR-related indicators such as leave patterns,deployment history, duty schedules, transfer frequency, training commitments, and workload trends. • Support optional self-reporting and wellness assessments through a secure mobile application. • Incorporate voluntary biometric and wellness data, where authorized and legally permissible. • Detect behavioral patterns associated with elevated stress risk. • Generate risk assessments and welfare recommendations for authorized welfare officers and commanders. • Enable proactive counseling, welfare interventions, and workload balancing measures. The system must be designed with strong privacy safeguards and focus on welfare support rather than disciplinary actions. • Expected Solution Develop an AI-driven predictive analytics platform comprising: • Personnel Wellness Monitoring Dashboard. • Mobile-based Wellness and Self-Assessment Application. • Predictive Behavioral Analytics Engine. • Stress and Burnout Risk Prediction Models. • Welfare Intervention Recommendation System. • Role-based Access Control and Privacy Management Framework. • Automated Alerts for authorized welfare personnel. • Data anonymization and secure storage mechanisms. The solution should identify trends and risk factors while ensuring that individual dignity, confidentiality, and data protection requirements are maintained. • Expected Benefits 1. Early identification of personnel requiring welfare support. 2. Reduction in stress-related incidents and operational fatigue. 3. Improved mental well-being and workforce resilience. 4. Enhanced readiness and operational effectiveness. 5. Better workload distribution and personnel management. 6. Improved retention and job satisfaction. 7. Data-driven welfare planning and resource allocation. 8. Reduction in incidents arising from prolonged occupational stress. • Preliminary Scope 1. Development of predictive behavioral analytics algorithms. 2. Mobile-based wellness self-reporting platform. 3. AI-driven stress and burnout risk assessment engine. 4. Commander and Welfare Officer dashboard. 5. Automated intervention recommendation system. 6. Secure integration with HRMS and personnel management systems. 7. Privacy-preserving analytics and role-based access controls. • Key Technical Challenges 1. Ensuring privacy and confidentiality of sensitive personnel data. 2. Preventing stigmatization of personnel identified as potentially at risk. 3. Minimizing false positives and false negatives in risk prediction. 4. Ensuring ethical and transparent AI decision-making. 5. Securing highly sensitive psychological and welfare-related information against cyber threats. 6. Building trust among personnel regarding system usage and data protection. • Strategic Importance • Enhances force readiness and personnel welfare. • Supports evidence-based welfare management. • Strengthens organizational resilience and operational effectiveness. • Promotes preventive mental health care rather than reactive interventions. • Creates an indigenous capability tailored to the unique operational and cultural environment of Indian CAPFs and Armed Forces. • Potential Market 1. Central Armed Police Forces (CAPFs). 2. Indian Armed Forces. 3. State Police Organizations. 4. Disaster Response and Emergency Services. 5. Government Organizations with high-stress workforces. 6. Corporate Human Resource and Employee Wellness Platforms. 7. International security and workforce welfare markets. • Expected Impact The proposed AI-enabled Personnel Stress and Welfare Monitoring System will help transform welfare management from a reactive process to a proactive and preventive framework. By enabling early identification of stress indicators and facilitating timely interventions,the solution can improve personnel well-being, enhance operational effectiveness, and strengthen the long-term resilience of uniformed services while maintaining the highest standards of privacy, ethics, and data security.

## What the brief never says

- specific machine learning algorithms or frameworks to use
- exact HRMS database schemas for integration
- the specific clinical scales or questionnaires required for self-assessment
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- The organisers attached a dataset, so build against theirs rather than inventing one.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

---
Problem statement facts assembled by Wynit from Ministry of Home Affairs's published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (629 words)

• Background Personnel serving in Central Armed Police Forces (CAPFs), Armed Forces, and other uniformed services operate under physically demanding, psychologically stressful, and often hazardous conditions.Extended deployments, operational pressures, separation from families,irregular working hours, and exposure to traumatic incidents can significantly impact mental well-being.Currently, stress identification largely depends on manual observation and self-reporting, which may delay timely intervention. There is a need for a proactive, technology-driven solution that can identify early indicators of stress, burnout, and psychological distress while maintaining privacy and organizational trust. • Description The proposed solution aims to develop an AI-powered Personnel Stress and Welfare Monitoring System capable of identifying potential indicators of stress, burnout, emotional fatigue, and welfare concerns through analysis of organizational and voluntarily provided wellness data.The system should: • Analyze HR-related indicators such as leave patterns,deployment history, duty schedules, transfer frequency, training commitments, and workload trends. • Support optional self-reporting and wellness assessments through a secure mobile application. • Incorporate voluntary biometric and wellness data, where authorized and legally permissible. • Detect behavioral patterns associated with elevated stress risk. • Generate risk assessments and welfare recommendations for authorized welfare officers and commanders. • Enable proactive counseling, welfare interventions, and workload balancing measures. The system must be designed with strong privacy safeguards and focus on welfare support rather than disciplinary actions. • Expected Solution Develop an AI-driven predictive analytics platform comprising: • Personnel Wellness Monitoring Dashboard. • Mobile-based Wellness and Self-Assessment Application. • Predictive Behavioral Analytics Engine. • Stress and Burnout Risk Prediction Models. • Welfare Intervention Recommendation System. • Role-based Access Control and Privacy Management Framework. • Automated Alerts for authorized welfare personnel. • Data anonymization and secure storage mechanisms. The solution should identify trends and risk factors while ensuring that individual dignity, confidentiality, and data protection requirements are maintained. • Expected Benefits 1. Early identification of personnel requiring welfare support. 2. Reduction in stress-related incidents and operational fatigue. 3. Improved mental well-being and workforce resilience. 4. Enhanced readiness and operational effectiveness. 5. Better workload distribution and personnel management. 6. Improved retention and job satisfaction. 7. Data-driven welfare planning and resource allocation. 8. Reduction in incidents arising from prolonged occupational stress. • Preliminary Scope 1. Development of predictive behavioral analytics algorithms. 2. Mobile-based wellness self-reporting platform. 3. AI-driven stress and burnout risk assessment engine. 4. Commander and Welfare Officer dashboard. 5. Automated intervention recommendation system. 6. Secure integration with HRMS and personnel management systems. 7. Privacy-preserving analytics and role-based access controls. • Key Technical Challenges 1. Ensuring privacy and confidentiality of sensitive personnel data. 2. Preventing stigmatization of personnel identified as potentially at risk. 3. Minimizing false positives and false negatives in risk prediction. 4. Ensuring ethical and transparent AI decision-making. 5. Securing highly sensitive psychological and welfare-related information against cyber threats. 6. Building trust among personnel regarding system usage and data protection. • Strategic Importance • Enhances force readiness and personnel welfare. • Supports evidence-based welfare management. • Strengthens organizational resilience and operational effectiveness. • Promotes preventive mental health care rather than reactive interventions. • Creates an indigenous capability tailored to the unique operational and cultural environment of Indian CAPFs and Armed Forces. • Potential Market 1. Central Armed Police Forces (CAPFs). 2. Indian Armed Forces. 3. State Police Organizations. 4. Disaster Response and Emergency Services. 5. Government Organizations with high-stress workforces. 6. Corporate Human Resource and Employee Wellness Platforms. 7. International security and workforce welfare markets. • Expected Impact The proposed AI-enabled Personnel Stress and Welfare Monitoring System will help transform welfare management from a reactive process to a proactive and preventive framework. By enabling early identification of stress indicators and facilitating timely interventions,the solution can improve personnel well-being, enhance operational effectiveness, and strengthen the long-term resilience of uniformed services while maintaining the highest standards of privacy, ethics, and data security.