Elderly Human Recognition System

This project is an Elderly Human Recognition System.Human Activity Recognition 70+ (HAR70+) dataset is a professionally-annotated dataset containing 18 fit-to-frail older-adult subjects (70-95 years old) wearing two 3-axial accelerometers for around 40 minutes during a semi-structured free-living protocol. The sensors were attached to the right thigh and lower back. The Project designed to handle the uploading of files of patients data , stop predictions, and retrieve results through a web interface. The system uses Flask for the backend and HTML/CSS with JavaScript for the frontend.

Table of Contents

• Machine learning
• Installation
• Usage
• Folder Structure

Machine learning

Data Collection and Cleaning

• Collected multiple CSV files containing accelerometer data.
• Loaded data into Pandas DataFrames and cleaned by removing duplicates and unnecessary columns (e.g., timestamps).

Data Exploration and Visualization

• Visualized activity distribution using pie charts and bar plots to understand data balance.
• Explored relationships between variables through scatter plots and correlation matrices.

Model Training and Evaluation

• Split data into training and testing sets.
• Trained classifiers (Logistic Regression, Decision Trees, Random Forests) and evaluated using metrics (accuracy, confusion matrices).
• Tuned hyperparameters for better model performance.

Model Persistence

• Saved the best model (Random Forest) using joblib for future use.

Prediction on New Data

• Demonstrated how to load the saved model and make predictions on new accelerometer data.

LSTM Model Training

• Implemented an LSTM model using TensorFlow/Keras for sequence data.
• Trained the LSTM model, monitored performance metrics (e.g., loss, accuracy).

Installation

Steps

1. Clone the repository:
   ```bash
   git clone https://github.com/HaroonMalik771/Human_activity_recognition_system.git

   ```

2. Create a virtual environment and activate it:
   bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

3. Install the required packages:
   bash pip install -r requirements.txt

4. Run the Flask application:
   bash python app.py

5. Open your web browser and go to http://127.0.0.1:5000/.

Usage

Upload a File

1. Click on the "Choose File" button in the "Upload a File" section.
2. Select a CSV file from your local machine.
3. Click on the "Upload" button. A popup message will display "Your file is uploaded successfully."

Stop Prediction

1. Enter the File ID in the "Stop Prediction" section.
2. Click on the "Stop" button to stop the prediction for the entered File ID.

Get Results

1. Enter the File ID in the "Get Results" section.
2. Click on the "Get Results" button to be redirected to the results page.

Folder Structure

project-root/
│
├── models/
│   └── lstm_model.h5             # Saved LSTM model
│
├── test_data/
│   └── new.csv                   # Example test data file
│
├── web/
│   ├── static/
│   │   └── images/
│   │       └── Screenshot.png    # Placeholder image
│   │
│   ├── templates/
│   │   ├── index.html            # HTML template for main interface
│   │   └── results.html          # HTML template for results page
│   │
│   ├── app.py                    # Main Flask application
│   └── requirements.txt          # List of Python packages required
│
└── README.md                     # Project README file