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| #!/bin/bash | ||
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| # Health Monitoring System Manual | ||
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| echo "==============================================" | ||
| echo " Health Monitoring System - User Manual" | ||
| echo "==============================================" | ||
| echo "" | ||
| echo "This script describes the functionality and usage of the Health Monitoring System implemented in Python." | ||
| echo "" | ||
| echo "--------------------------" | ||
| echo "System Overview:" | ||
| echo "--------------------------" | ||
| echo "The Health Monitoring System is a Python-based application designed to simulate, analyze, and monitor health metrics." | ||
| echo "Key Features include:" | ||
| echo " 1. Synthetic Data Generation for heart rate, step count, and activity." | ||
| echo " 2. Anomaly Detection using DBSCAN clustering." | ||
| echo " 3. Activity Classification using a Random Forest model." | ||
| echo " 4. Real-Time Data Simulation." | ||
| echo " 5. Visualization of health trends and detected anomalies." | ||
| echo "" | ||
| echo "--------------------------" | ||
| echo "System Steps & Functionality:" | ||
| echo "--------------------------" | ||
| echo "1. Generate Data:" | ||
| echo " - Simulates health data for heart rate, step count, and activity over time." | ||
| echo " - Adds timestamps to each data point." | ||
| echo "" | ||
| echo "2. Preprocess Data:" | ||
| echo " - Normalizes the features (heart rate and step count) using StandardScaler for better model performance." | ||
| echo "" | ||
| echo "3. Train Activity Classification Model:" | ||
| echo " - Splits the data into training and test sets." | ||
| echo " - Uses Random Forest Classifier with cross-validation for training." | ||
| echo " - Evaluates the model with metrics such as accuracy, precision, recall, and F1-score." | ||
| echo "" | ||
| echo "4. Detect Anomalies:" | ||
| echo " - Applies DBSCAN clustering to detect outliers in the health data." | ||
| echo " - Marks outliers as anomalies and visualizes them in a time-series plot." | ||
| echo "" | ||
| echo "5. Simulate Real-Time Data:" | ||
| echo " - Samples random data points from the dataset to simulate real-time health monitoring." | ||
| echo " - Displays heart rate, step count, activity type, and anomaly status in real-time." | ||
| echo "" | ||
| echo "6. Visualize Health Metrics:" | ||
| echo " - Plots time-series data for heart rate and step count." | ||
| echo " - Highlights anomalies for easy identification of irregularities." | ||
| echo "" | ||
| echo "--------------------------" | ||
| echo "Instructions to Run:" | ||
| echo "--------------------------" | ||
| echo "1. Ensure Python 3.x and the required libraries (numpy, pandas, matplotlib, sklearn) are installed." | ||
| echo " To install the libraries, run:" | ||
| echo " pip install numpy pandas matplotlib scikit-learn" | ||
| echo "" | ||
| echo "2. Copy the Python code into a file (e.g., health_monitor.py)." | ||
| echo "" | ||
| echo "3. Run the Python script:" | ||
| echo " python health_monitor.py" | ||
| echo "" | ||
| echo "4. Follow the output to observe model training results, anomaly detection, and real-time data simulation." | ||
| echo "" | ||
| echo "--------------------------" | ||
| echo "Additional Notes:" | ||
| echo "--------------------------" | ||
| echo "- Modify the 'data_size' parameter in the Python code to adjust the size of the simulated dataset." | ||
| echo "- Customize the thresholds in the DBSCAN algorithm for different sensitivity levels." | ||
| echo "- Extend the system to integrate real-world data sources or send alerts for anomalies." | ||
| echo "" | ||
| echo "==============================================" | ||
| echo " End of Manual" | ||
| echo "==============================================" |