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Original by Nithish G

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Build a working prototype web/mobile application called "OsteoSense NER" for SIH26004: AI-Assisted Early Detection System for Osteoarthritis (OA) Risk Markers in the North Eastern Region.

IMPORTANT:
This is a SCREENING and RISK-ASSESSMENT prototype, NOT a medical diagnosis system. Never claim that the AI can definitively diagnose osteoarthritis. The result should say "OA Risk Indicators" and recommend professional clinical evaluation for high-risk cases.

TARGET USERS:
Primary healthcare workers, community health workers, and rural health-camp staff in remote areas of North Eastern India.

CORE IDEA:
Create a low-cost, portable OA screening workflow combining:
1. Smartphone camera-based gait analysis
2. Wearable knee IMU sensor data simulation
3. Pain and mobility questionnaire
4. AI-based risk scoring
5. Personal movement baseline and trend monitoring
6. Offline-first data storage
7. Multilingual and simple healthcare-worker interface

BUILD THESE SCREENS:

1. LOGIN / HOME DASHBOARD
- App name: OsteoSense NER
- Subtitle: AI-Assisted Osteoarthritis Risk Screening
- Buttons:
  "New Screening"
  "Patient Records"
  "Risk Dashboard"
  "Sync Data"
  "Settings"
- Show Offline Mode status clearly.

2. PATIENT REGISTRATION
Fields:
- Patient ID
- Name
- Age
- Gender
- Village / Location
- Phone number (optional)
- Previous knee/hip injury
- Occupation / physical workload
- Previous OA diagnosis (Yes/No)
- Save patient

Do not collect unnecessary sensitive information.

3. PAIN & MOBILITY SCREENING
Create simple questions with large buttons:
- Knee/hip pain during walking?
- Joint stiffness after rest?
- Difficulty climbing stairs?
- Difficulty walking long distances?
- Difficulty standing for a long time?
- Does pain affect daily activities?

Use simple choices:
None / Mild / Moderate / Severe

Calculate a simple symptom score for prototype demonstration.

4. KNEE MOVEMENT TEST
Show instructions:
"Place the wearable IMU knee band securely around the knee."
"Calibrate before starting."
"Slowly bend and straighten the knee."

For prototype purposes, create a simulated live sensor panel showing:
- Knee angle
- Angular velocity
- Acceleration
- Range of Motion
- Movement symmetry

Add a "Start Test" button and animate changing sensor values.

Show a simple real-time knee-angle graph.

Also include:
"Calibrate Sensor"
"Start Test"
"Stop Test"

5. CAMERA GAIT ANALYSIS
Create a screen showing a camera preview placeholder.

Instructions:
"Place the smartphone on a stable stand."
"Ensure the full body is visible."
"Ask the patient to walk along the marked 5–10 metre path."

Create a prototype gait-analysis visualization using a human silhouette/skeleton with:
- Head
- Hip
- Left knee
- Right knee
- Left ankle
- Right ankle

Show simulated detected values:
- Walking speed
- Step count
- Step timing
- Left/right symmetry
- Stride consistency
- Knee movement

Add:
"Start Walking Analysis"

After the test, display:
"Gait analysis completed."

IMPORTANT:
Clearly label simulated/demo sensor values as "Prototype Simulation" when real hardware/camera processing is not connected.

6. AI RISK ANALYSIS
Create a clear processing screen:

Pain Score
+
Knee Movement
+
Range of Motion
+
Gait Symmetry
+
Walking Pattern
+
Mobility Score
        ↓
AI RISK ANALYSIS
        ↓
OA RISK INDICATORS

Use three risk levels:
GREEN - Low Risk Indicators
YELLOW - Moderate Risk Indicators
RED - High Risk Indicators

Generate a risk percentage for demonstration.

Example:
OA Risk Indicator Score: 72%
Risk Level: HIGH

Do NOT call this "OA diagnosis."

7. EXPLAINABLE AI RESULT
Show WHY the risk score was generated.

Example:

OA RISK INDICATORS: HIGH

Contributing indicators:
✓ Reduced knee range of motion
✓ Increased gait asymmetry
✓ Reported moderate pain
✓ Reduced mobility
✓ Change from personal baseline

Show a simple horizontal contribution chart.

Add disclaimer:
"This screening result is not a medical diagnosis. Please consult a qualified healthcare professional for clinical evaluation."

8. PERSONAL BASELINE & TREND
Create a graph showing screening results over multiple visits.

Example:

Visit 1 → 32% Low
Visit 2 → 38% Low
Visit 3 → 49% Moderate
Visit 4 → 61% Moderate
Visit 5 → 72% High

Title:
"Personal OA Risk Indicator Trend"

Important innovation:
Compare the patient's current movement with their own previous baseline instead of relying only on population averages.

Show:
"Movement deviation from personal baseline"

9. PATIENT REPORT
Generate a professional digital screening report containing:
- Patient ID
- Date
- Symptoms summary
- Knee movement summary
- Gait summary
- OA risk indicator score
- Risk level
- Main contributing indicators
- Personal baseline trend
- Recommended next step

For HIGH risk:
"Clinical evaluation recommended."

For MODERATE:
"Follow-up screening and healthcare professional evaluation may be considered."

For LOW:
"Continue preventive joint-health practices and routine monitoring."

10. PATIENT RECORDS
Create searchable patient records.

Each record should show:
Patient ID
Name
Last screening date
Current risk level
Risk trend

Allow opening a patient's history.

11. OFFLINE-FIRST MODE
The app must visually support:
"Offline Mode"

When internet is unavailable:
- Save screening records locally
- Continue screening
- Continue AI prototype assessment
- Queue records for synchronization

When connection returns:
"Sync Pending Records"
button becomes available.

12. MULTILINGUAL SUPPORT
Add a language selector:
English
Hindi
Assamese
Bengali

Use simple language for instructions.

13. HEALTHCARE WORKER DASHBOARD
Create summary cards:
- Total patients screened
- Low risk
- Moderate risk
- High risk
- Follow-up required

Create a simple location-based risk visualization/map mockup for villages/health camps in NER.

14. PREVENTIVE AWARENESS SECTION
Create a general education section covering:
- Joint-friendly physical activity
- Maintaining mobility
- Healthy lifestyle
- When to seek professional evaluation

Do not provide personalized medical treatment plans.

DESIGN:
- Clean modern healthcare UI
- Mobile-first responsive design
- Large buttons suitable for field healthcare workers
- Simple icons
- High readability
- Minimal text per screen
- Clear green/yellow/red risk indicators
- Professional but friendly
- Designed for low-resource rural healthcare environments

PROTOTYPE HARDWARE INTEGRATION PLACEHOLDERS:
Create an "Hardware Status" panel showing:
- ESP32: Connected / Demo
- Knee IMU: Connected / Demo
- Smartphone Camera: Ready
- Bluetooth: Connected / Demo
- Battery: 82%

The hardware values can be simulated for this prototype.

TECHNICAL BEHAVIOR:
- Use realistic simulated sensor data.
- Make graphs animate when tests start.
- Calculate a transparent prototype risk score using weighted inputs.
- Store patient screening history.
- Show trend changes between visits.
- Make the interface work even when offline.
- Clearly mark simulated hardware data.
- Keep all medical claims conservative.
- Do not present the prototype as clinically validated.

MOST IMPORTANT DIFFERENTIATING FEATURE:
Implement "Personal Baseline AI":
The system should establish the patient's initial movement/gait baseline and compare future screening sessions against that individual's own baseline. If movement asymmetry, range of motion, or gait characteristics deviate progressively from the baseline, increase the OA risk-indicator score and highlight "Movement deviation from personal baseline."

FINAL OUTPUT:
Create a fully navigable working prototype with realistic demo data, interactive buttons, simulated sensor readings, gait visualization, AI risk scoring, patient records, trend graphs, offline mode, multilingual UI, and downloadable/viewable screening reports.

App title:
"OsteoSense NER"

Tagline:
"Portable AI-Assisted OA Risk Screening for Remote Communities"

Start fresh from this prompt

Builds a brand new app from the first prompt only, so later changes to the original are not included.