Symtrack: Turning Symptoms into Signals

Problem Statement:
In healthcare, one of the biggest gaps lies in patient-reported symptoms. Many times, patients forget small details, underreport changes, or wait until symptoms become severe. This delay makes diagnosis harder, treatments less effective, and recovery longer. Without proper symptom tracking, doctors often get only half the story.

Proposed Solution:
Symtrack is a digital symptom tracker that works like a smart health journal. Patients log their daily symptoms, and the system uses data visualization and pattern recognition to highlight trends. For example, it can show if headaches are getting more frequent, if blood sugar variations align with lifestyle changes, or if certain triggers worsen conditions. Symtrack transforms scattered symptom notes into actionable medical insights, helping doctors make more accurate decisions, faster.

Who Benefits:

Patients → Early detection of health changes, better self-awareness.

Doctors → Reliable, structured data for accurate diagnosis.

Healthcare systems → Reduced costs by preventing late-stage complications.

Why This Matters to Me:
As an engineering student, I’m fascinated by how small patterns can reveal big truths. Just like engineers track data to predict failures in machines, we can track human symptoms to prevent health crises. Symtrack matters to me because it combines my love for problem-solving with my desire to improve lives—making healthcare not just reactive, but truly proactive.

One of the most pressing challenges in epilepsy today is the lack of predictive diagnosis. Patients live under constant uncertainty, as seizures strike without warning. My idea is NeuroBand: a lifestyle-looking hairband that integrates hidden EEG electrodes and a compact AI chip.

The band continuously records brain activity and runs a machine learning model trained on epilepsy datasets. Its goal is to predict seizures at least two minutes in advance—a breakthrough, since even such a short window can allow patients to take fast-acting medication or move to a safe place.

The concept is simple but transformative. From the outside, NeuroBand looks like a regular fashion accessory, not a medical device—making it comfortable and socially acceptable. Inside, it’s a continuous brain-monitoring and early-warning system. Notifications are sent directly to the patient’s phone, ensuring seamless usability.

The impact is huge:

• Patients gain freedom and confidence.

• Families and caregivers experience less anxiety.

• Doctors gain continuous EEG data for better treatment.

While challenges exist—ensuring data accuracy, building ergonomic design, and real-time processing—the potential of NeuroBand lies in giving people something they’ve never had before: predictive control over epilepsy.

Introducing VitalLink—a smart, AI-powered solution designed exclusively for the elderly. We focus on keeping seniors safe, healthy, and independent, while giving families peace of mind every moment of the day

Elderly people are highly vulnerable to sudden medical emergencies such as heart attacks, strokes, or accidental falls. Many live alone and cannot always call for help when every second counts. Families often live far away, and by the time emergency services are contacted, it may be too late. These delays lead to avoidable hospitalizations, long-term complications, or even fatalities. The lack of timely monitoring creates constant stress for both seniors and their families, reducing the sense of independence and safety for older adults.

Solution:

A comprehensive AI-powered wearable device with a connected mobile app provides proactive protection and peace of mind. The wearable continuously monitors vital signs such as heart rate, oxygen levels, and movement patterns. Using machine learning, it can not only detect emergencies in real time but also predict potential risks in advance by analyzing health trends. When danger is detected, the system immediately alerts family members, nearby doctors, and ambulance services with the senior’s live location. Additional features include medicine reminders, voice-based AI assistance in local languages, and health logs for medical professionals, making care more accessible and actionable.

Target Audience:

Elderly individuals, their families, and healthcare providers.

Why it Matters to Me:

Because leveraging AI can save precious minutes during emergencies, reduce health risks, and provide seniors with independence, safety, and dignity, while offering families peace of mind.

 Gluco Vision – Non-Invasive Glucose Estimation Through Video

1. THE DREAM: Accessible Health for Everyone

One of the major growing diseases in the world,especially in India is Diabetes.Thousands and Millions of people live with diabetes or are at risk, but regular monitoring remains a challenge. The most traditional methods require finger-prick tests, which are painful, inconvenient, and costly.

For poor people who can afford the bare minimum, the financial burden is significant: a glucometer can cost between ₹800 and ₹1500, and test strips cost  between ₹15 and ₹30 each. This can reach between ₹1,000 and ₹2,500 per month for patients needing multiple daily tests. Due to the cost,most low income people frequently skip testing,which then increases the health complications such as heart diseases etc. which can even lead to death.

tThe idea with Gluco Vision is to make glucose monitoring affordable for everyone: a solution that is non-invasive, low-cost, and widely accessible, requiring only a smartphone or laptop camera.

2. The Problem: Painful, Expensiv

e, and Fragmented Monitoring

There are many problems created due to the traditional glucose monitoring:

• Invasiveness: Frequent pricking is painful and discourages adherence.
  
  
• High Cost: particularly for low income and poor people,continuous use of strips is expensive,
  
  
• Limited Insight: Finger prick tests only provide a snap
  shot of glucose levels,which are missing fluctuations throughout the day.
  
  
• Stress and Anxiety: it can lead to decrease of quality of life due to daily monitoring diseases

These limitations contribute to poor disease management, leading to hospitalization and diseases which can be avoidable.This mainly affects the poor.

3. Our Solution: Gluco Vision

Gluco Vision uses the power of photoplethysmography (PPG) which are video based  and machine learning to estimate blood glucose non-invasively, using just a camera and a smartphone.

Key Technical Highlights:

• • PPG Signal Extraction: Captures subtle color changes in fingertips from video, corresponding to blood volume changes.
    

• Signal Processing: to reduce noise from motion or lighting we apply Butterworth bandpass filtering and Savitzky-Golay smoothing
  
  
• Feature Engineering: To relate PPG signals with glucose dynamics,it computes heart rate, entropy, signal area, and other physiological features
  
  
• Data Augmentation: Generates synthetic glucose data to enhance training, combining real and simulated data for a better result .
  
  
• Machine Learning Model:To predict glucose levels with associated risk categories (normal, elevated, high),we Use Gradient Boosting Regressor 

Example Output:

 Model trained with enhanced real and synthetic data. Test MSE: 92.7
  Estimated Glucose: 142.3 mg/dL  Elevated glucose – Monitor or consult physician

Gluco Vision significantly reduces costs and makes regular monitoring feasible for low-income patients because we are eliminating the need for physical test strips.

4. Socio-Economic Impact

• • Budget friendly Healthcare: Eliminates the recurring expenses of strips and lancets.
    
    
  • Improved Compliance: encourages regular checks due to non-invasive monitoring.
    
    
  • Early Detection:reduces complications. Continuous or frequent monitoring which  allows for early intervention
    
    

• Accessible Anywhere: makes it feasible in remote or underserved areas as only a camera and internet-enabled device are required,
  
  

5. Future Roadmap

• Face and Fingertip Tracking: For easier, automated signal capture.
  
  
• Multiple Video Formats Support: Ensures compatibility across devices.
  
  
• Integration with Real-World Datasets: For better accuracy and better results we use real world datasets
  
  
• Advanced AI Models:  To improve predictive performance and accuracy we use Neural networks and deep learning
• Continuous Monitoring Potential: Enable long-term trends for proactive healthcare.