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+# 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
+```
+