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IoT & Machine Learning
Stress Monitoring System
An IoT-based project that monitors and predicts stress levels using PPG sensors and ML.
Client
Confidential
Duration
5 months
Role
Full Stack Development
Overview
This hardware-software hybrid project collects real-time biometric data via PPG sensors. It uses machine learning models to analyze heart rate variability and accurately predict the user's stress state.
The Problem
Current stress monitoring tools are either too expensive or lack predictive real-time analysis.
Our Solution
Developed an affordable IoT device paired with a robust ML model to continuously monitor and predict stress.
Key Features
- Real-time Biometric Tracking
- Hardware-Software Integration
- Stress Level Prediction
- Data Visualization
Technologies Used
PythonIoTArduino/ESP32Machine Learning
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