LSTM-Human-Activity-Recognition and RNN-for-Human-Activity-Recognition-using-2D-Pose-Input

About LSTM-Human-Activity-Recognition

guillaume-chevalier/LSTM-Human-Activity-Recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

# Technical Summary Employs a many-to-one LSTM architecture that processes 128-sample time windows of 9-channel inertial sensor data (3-axis accelerometer and gyroscope readings) without extensive feature engineering, relying instead on the recurrent network to automatically learn temporal patterns across sequential measurements. Minimal preprocessing is applied beyond gravity filtering, contrasting with traditional signal-processing-heavy approaches that require manual feature extraction. Built with TensorFlow and includes Jupyter notebook implementations demonstrating end-to-end data loading, model training, and evaluation metrics on the UCI HAR Dataset.

About RNN-for-Human-Activity-Recognition-using-2D-Pose-Input

stuarteiffert/RNN-for-Human-Activity-Recognition-using-2D-Pose-Input

Activity Recognition from 2D pose using an LSTM RNN