mkowalsky0/human_activities_recognition

:microscope: Checking how the CNN neural network deals with HAR using inertial sensors from the smartphone.

Human Activities Recognition Mobile Application

Hi, I'm Mike. Welcome in my project!

Introduction

• 📱 the mobile application for Android Systems (v. 4.0 and higher)

• 👨‍🔬 using Convuctional Neural Networks to classificate motion activities

• 📚 using Smartphone-Based Recognition of Human Activities and Postural Transitions Data Set for the training and testing CNN model

• 📳 real-time inertial signals registration (based on accelerometer and gyroscope)

Technologies

• Python v. 3.10.1
• TensorFlow v. 2.9.1 n Keras
• Jupyter Notebook
• Java v. 8
• Android Studio v. 2020.3.1

Description

This project has three main steps:

1. Signal Processing

raw signals processing, noises filtering: 3rd order Butterworth filter, separating the gravity component: high-pass filter, windowing signals, normalisation, division of the data into training and testing components, matching the shape to the CNN model, signals visualisation

• Example of the Data Frame with values after the signals processing:

• Histogram with the obtained data samples:

2. CNN Model Design

designing a CNN model, training and testing process, fitting and evaluating, parameters visualisation

• CNN model:

• Quality of the CNN model:

🔬 MODEL ACCURACY: 95,03%

3. Mobile Application

the mobile application based on CNN model to clasificate 6 motion activities in real-time:

1. WALKING 2. WALKING UPSTAIRS 3. WALKING DOWNSTAIRS 4. SITTING 5. STANDING 6. LAYING

• Application Interface:

🙋 How to place the phone on the body:

📱 Phone axes:

Conclusions

The application has been tested on a group of people aged 25, 27 and 45. The results of the above program were satisfactory. The application is very good at classifying activities with indexes 1, 3, 4 and 6, while it makes occasional errors when classifying activities with indexes 2 and 5. This model is an excellent basis for further research into creating a useful application for motion recognition.

License

MIT License

I'M GLAD FOR YOUR ATTENTION!