https://github.com/aditya-xq/har-test

A test project to explore smartwatch data simulation and human activity recognition
https://github.com/aditya-xq/har-test

human-activity-recognition python simulation wearable-sensors

Last synced: about 5 hours ago
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A test project to explore smartwatch data simulation and human activity recognition

• Host: GitHub
• URL: https://github.com/aditya-xq/har-test
• Owner: aditya-xq
• Created: 2022-12-11T13:45:33.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-09T14:07:08.000Z (over 1 year ago)
• Last Synced: 2024-11-11T22:07:32.257Z (about 2 months ago)
• Topics: human-activity-recognition, python, simulation, wearable-sensors
• Language: Python
• Homepage:
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SmartWatch Activity Simulator 🕰️

Hello and welcome to the **SmartWatch Activity Simulator**! This fun little test project simulates the data generation of a smartwatch's accelerometer and gyroscope. But that's not all; it also predicts human activity based on this data! 

## How it works:

1. **SmartWatchSimulator.py:** This file contains a class `Smartwatch` which can generate random accelerometer and gyroscope data, simulating the kind of data a real smartwatch might capture.

2. **App.py:** This is where the action happens! It uses the `Smartwatch` class to generate data and then predicts the activity using a simple heuristic. The results are printed out for you to see.

## How to use:

1. Run the `App.py` script.

2. Watch the console! You'll see generated data from the smartwatch's sensors and then a prediction of the human activity based on that data.

3. It will keep running, generating a new prediction every second. If you want to stop, simply press `CTRL + C` or close the console.

## Future Directions:

🚀 **Machine Learning Integration:** Instead of using simple heuristics, we can integrate a machine learning model to predict activities based on the data for even more accurate predictions!

🎨 **GUI Implementation:** A simple graphical user interface could be added to visualize the data and predictions in a more user-friendly manner.

🌍 **Additional Sensors:** We can simulate other sensors such as heart rate monitors or GPS to generate even richer datasets.

🕵️ **Anomaly Detection:** Beyond just predicting activities, we can detect unusual patterns in the data, potentially useful for health monitoring or fall detection.

## Conclusion:

This project is ideal for anyone new to programming and wanting a sneak peek into how smartwatch data works or for those wanting to experiment with their own activity prediction algorithms.

**Happy Coding!** 🚀👩‍💻👨‍💻🎉