TCN for Human Activity Recognition

This project implements a deep learning model that recognizes six different human activities (walking, walking upstairs, walking downstairs, sitting, standing, laying) from accelerometer and gyroscope sensor data. The model uses dilated causal convolutions to capture temporal patterns in the sensor readings.