Smart Posture Tracking System: Preventing Neck Pain with Computer Vision

I recently developed a posture tracking system that helps users maintain healthy neck posture while working. This project combines hardware (Raspberry Pi), computer vision (YOLOv11n), and real-time processing to create an automated posture monitoring solution.

Project Overview

The system continuously monitors user posture and provides timely alerts to prevent neck strain and related discomfort. It's designed to be unobtrusive yet effective, running silently in the background until intervention is needed.

Technical Implementation

Hardware Setup

Raspberry Pi: Acts as the core processing unit
PiCamera2: Provides continuous image capture capability
Optional LED: Visual indicator for posture alerts

Software Components

Core Language: Python
Computer Vision: Ultralytics YOLOv11n NCNN model
Image Processing: Real-time capture and analysis pipeline

How It Works

Continuous Monitoring
- The PiCamera2 captures images at regular intervals
- Images are processed in real-time without storing
- System maintains minimal resource usage
Posture Detection
- YOLOv11n model analyzes each frame
- Detects head position and angle
- Tracks duration of fixed postures
Alert System
- Triggers alerts when fixed posture exceeds threshold
- Provides user-friendly notifications
- Encourages regular posture breaks

Technical Challenges and Solutions

Model Optimization

The YOLOv11n NCNN model was chosen for its balance of accuracy and performance on the Raspberry Pi. It provides reliable posture detection while maintaining reasonable processing speeds on limited hardware.

Real-time Processing

To achieve real-time monitoring without overwhelming the Raspberry Pi:

Optimized image capture settings
Implemented efficient processing pipeline
Minimized unnecessary computations

Privacy Considerations

The system processes all data locally on the Raspberry Pi, ensuring user privacy:

No images are stored
No data leaves the device
Real-time processing only

Future Improvements

Enhanced Detection
- Add shoulder position tracking
- Implement multiple posture type detection
- Include sitting position analysis
User Interface
- Develop a companion mobile app
- Add customizable alert settings
- Include posture statistics dashboard
Hardware Optimization
- Explore battery-powered operation
- Minimize form factor
- Add wireless connectivity options

Code Implementation

Core System Setup

from picamera2 import Picamera2
from ultralytics import YOLO
import cv2
import time

class PostureTracker:
    def __init__(self):
        # Initialize camera
        self.camera = Picamera2()
        self.camera.configure(self.camera.create_preview_configuration())
        self.camera.start()

        # Load YOLO model
        self.model = YOLO('yolov8n.pt')
        
        # Posture tracking parameters
        self.fixed_posture_threshold = 30  # seconds
        self.last_movement_time = time.time()
        self.alert_active = False

Conclusion

This project demonstrates how accessible AI and computer vision technology can be applied to create practical solutions for everyday health concerns. The combination of Raspberry Pi's affordability and YOLO's capability makes it possible to build effective health monitoring tools at home.

By focusing on preventing neck pain through early intervention, this system helps users maintain better posture habits while working at their desks. The real-time feedback mechanism ensures that users take necessary breaks before discomfort sets in.

This project is open for collaboration and improvements. Feel free to reach out if you're interested in contributing or have suggestions for enhancements.

Building a Smart Posture Tracking System with Raspberry Pi and YOLO