From eed342936f78b9137f0e34c8089622c4926ddeee Mon Sep 17 00:00:00 2001
From: Stalin <161853795+Stalin-143@users.noreply.github.com>
Date: Tue, 10 Dec 2024 13:51:49 +0530
Subject: [PATCH] Create manual.sh

---
 Health Monitoring/manual.sh | 72 +++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 Health Monitoring/manual.sh

diff --git a/Health Monitoring/manual.sh b/Health Monitoring/manual.sh
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+#!/bin/bash
+
+# Health Monitoring System Manual
+
+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 "=============================================="