Human Activity Classification Using IMU Sensor Data

In this project, I developed a deep learning pipeline that classifies human activities based on accelerometer data from IMU sensors. The model accurately detects activities such as walking, eating, and drinking by leveraging PyTorch for model development, CUDA for GPU-accelerated training, and Python for data processing. This solution is designed to be easily deployable in wearable devices for activity monitoring, health tracking, and human behavior analysis applications.