Human Activity Classification Project

📌 Overview

This project focuses on Human Activity Recognition (HAR) using Smartphones, where study participants carried a smartphone equipped with an inertial sensor while performing daily activities. The goal is to classify these activities into one of six categories:

✅ Walking
✅ Walking Upstairs
✅ Walking Downstairs
✅ Sitting
✅ Standing
✅ Laying

This classification is achieved using machine learning techniques on sensor data collected from accelerometers and gyroscopes.

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📂 Dataset Information

The dataset contains sensor data from the Human Activity Recognition with Smartphones database. Each record includes:

🔹 Triaxial Acceleration – From the accelerometer (total acceleration & estimated body acceleration)
🔹 Triaxial Angular Velocity – From the gyroscope
🔹 561 Feature Vector – Containing time-domain and frequency-domain variables
🔹 Activity Label – Corresponding to one of the six activities

The data is scaled from -1 (minimum) to 1.0 (maximum) for better model performance.

📌 Sample Data Types (Last 5 Columns):

data.dtypes.tail()
angle(tBodyGyroJerkMean,gravityMean)    float64
angle(X,gravityMean)                    float64
angle(Y,gravityMean)                    float64
angle(Z,gravityMean)                    float64
Activity                                 object

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🚀 Project Objectives

✔️ Preprocess & Clean Data for ML models
✔️ Feature Engineering to extract meaningful patterns
✔️ Train Classification Models to predict activities
✔️ Evaluate Model Performance using accuracy and confusion matrices
✔️ Optimize Performance using hyperparameter tuning

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🛠️ Technologies Used

• Python 🐍
• Pandas, NumPy (Data Handling)
• Scikit-Learn (Machine Learning)
• Matplotlib, Seaborn (Data Visualization)
• Jupyter Notebook (Interactive Development)

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📊 Model Performance & Evaluation

📈 Metrics Used:
✅ Accuracy
✅ Precision
✅ Recall
✅ Confusion Matrix

The dataset is well-balanced, and the models are evaluated to ensure high classification accuracy across all activity categories.

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📁 Folder Structure

📂 Human-Activity-Classification  
 ├── 📁 data/                # Dataset Files  
 ├── 📁 notebooks/           # Jupyter Notebooks  
 ├── 📁 models/              # Trained Models  
 ├── 📄 README.md            # Project Documentation  
 ├── 📄 requirements.txt     # Dependencies  

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👨‍💻 How to Run the Project

1️⃣ Clone the Repository

git clone https://github.com/Tolumie/Human-Activity-Classification-Project.git
cd Human-Activity-Classification-Project

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Jupyter Notebook

jupyter notebook

4️⃣ Train & Evaluate the Model

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📌 Future Improvements

✅ Implement Deep Learning (LSTMs, CNNs) for better feature extraction
✅ Improve real-time activity detection using streaming data
✅ Optimize feature engineering for better classification accuracy

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📜 License

This project is open-source under the MIT License.

🔗 Contributions are welcome! Fork this repo and submit a PR.