# 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](#machine-learning)

- [Installation](#installation)

- [Usage](#usage)

- [Folder Structure](#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

```