Human Activity Detection Using Sensor Data

Neurathon AI/ML Hackathon

📌 Overview

This project focuses on Human Activity Recognition (HAR) using smartphone sensor data. The model classifies different human activities based on accelerometer and gyroscope data from mobile devices.

🚀 Features

✔️ Classifies activities like Walking, Sitting, Standing, and Jogging
✔️ Uses ML models to predict human actions based on sensor data
✔️ Achieves 93% accuracy

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📂 Dataset

The dataset used for this project is the UCI HAR Dataset. It contains motion sensor data collected from mobile devices while participants performed different activities.

📌 Dataset Files:

• train.csv - Training data
• test.csv - Testing data
• features.txt - Feature names

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🛠️ Tech Stack

🔹 Programming Language: Python
🔹 Libraries: Pandas, NumPy, Scikit-Learn
🔹 Model Used: Random Forest Classifier

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📊 Model Performance

Metric	Score
Accuracy	93%
Precision	XX%
Recall	XX%

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📌 How to Run the Project

🔹 1. Clone the Repository

git clone https://github.com/robinnits/Human-Activity-Detection.git
cd Human-Activity-Detection

🔹 2. Create a Virtual Environment (Recommended)

python3 -m venv venv  
source venv/bin/activate  # (Mac/Linux)  
venv\Scripts\activate  # (Windows)  

🔹 3. Install Dependencies

pip install -r requirements.txt

🔹 4. Run the Model

python train_model.py

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📚 Folder Structure

📂 Human-Activity-Detection
 ┣ 📂 data  
 ┃ ┣ 📜 train.csv  
 ┃ ┣ 📜 test.csv  
 ┣ 📂 models  
 ┃ ┣ 📜 trained_model.pkl  
 ┣ 📜 train_model.py  
 ┣ 📜 test_model.py  
 ┣ 📜 README.md  
 ┣ 📜 requirements.txt  

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👨‍💻 Contributors

👤 Robin Poddar
📌 NIT Silchar

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📌 Acknowledgments

• UCI Machine Learning Repository for providing the dataset.
• Scikit-Learn & Pandas for ML implementation.

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