Human Activity Recognition (HAR) involves predicting physical activities such as walking, sitting, standing, etc., using sensor data from devices like smartphones or wearables. This data is typically collected via accelerometers and gyroscopes, which measure the device’s movement and orientation. In this blog post, we will explore how to process sensor data, build a model, and visualize its performance to recognize human activities.
We will guide you through:
- Understanding the Data
- Visualizing Sensor Data
- Building a Machine Learning Model
- Evaluating Model Performance
- Real-Time Activity Prediction
Let’s get started!
In this project, we use the UCI HAR Dataset, a popular dataset for activity recognition. It contains data collected from smartphone accelerometer and gyroscope sensors while participants performed six activities: walking, sitting, standing, laying, walking upstairs, and walking downstairs.
Step-by-Step Breakdown:
- Import the necessary libraries.
# Import necessary…