An Unsupervised TinyML Approach with Efficient Edge AI for Effective Stress and Sleep Monitoring
Objective
To design and implement an unsupervised TinyML-based system that performs on-device stress and sleep monitoring using physiological sensor data with efficient edge AI processing. To develop a lightweight, energy-efficient model capable of detecting patterns and anomalies in real time without labeled data, enabling continuous, privacy-preserving health monitoring on embedded devices.
Abstract
The Unsupervised TinyML Approach with Efficient Edge AI for Effective Stress and Sleep Monitoring is developed to analyze human stress levels and sleeping patterns using embedded intelligence at the edge level. The system utilizes a GSR sensor to measure stress response, a pulse sensor to monitor heart rate, and a Dallas temperature sensor to observe body temperature variations. A web camera is employed to monitor facial and eye movement patterns for sleep detection using Dlib-based image processing techniques. A Raspberry Pi acts as the processing unit to collect and analyze physiological and visual data for evaluating sleep duration and stress conditions. The monitored parameters and sleep information are displayed on an LCD module for user observation. Whenever abnormal stress levels or irregular sleep behavior are identified, a buzzer alert is activated to notify the user. The proposed system enables efficient personal health monitoring through local data processing, intelligent analysis, and automated alert mechanisms.
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Block Diagram
Specifications
Hardware components:
· Raspberry Pi
· Memory Card
· Power Supply
· Adapter
· GSR Sensor
· Pulse Sensor
· Dallas Temperature Sensor
· Web Camera
· LCD
· Buzzer
Software requirements:
· Raspbian OS
· Python
Learning Outcomes
Learning Outcomes
- Understanding Raspberry Pi architecture and pin configuration
- Installation and setup of Raspberry Pi OS
- Software installation and system configuration for Raspberry Pi
- Introduction to Raspberry Pi development environment
- Basic programming using Python for embedded applications
- Fundamentals of Embedded Systems programming
- Basics of IoT platforms and cloud connectivity
- Understanding power supply and hardware interfacing
- Knowledge of sensor interfacing with Raspberry Pi
Project Development Life Cycle
- Planning and Requirement Gathering (hardware, software, and tools)
- Circuit and schematic preparation
- Program development and debugging
- Hardware interfacing and troubleshooting
- System integration and output testing
Practical Exposure
- Working with hardware and software tools
- Developing solutions for practical monitoring systems
- Individual and team-based project implementation
- Implementation of innovative and creative ideas
Skills Developed
- Embedded system development
- Problem analysis
- Problem solving
- Programming skills
- Creativity and innovation
- System deployment
- Testing and validation
- Debugging techniques
- Project presentation
- Technical documentation and thesis writing
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
