Classification on raw time series in TensorFlow with a LSTM RNN
Human Activity Recognition using TensorFlow and LSTM RNNs on smartphone sensor data to classify six movement types.
What is Classification on raw time series in TensorFlow with a LSTM RNN?
LSTM-Human-Activity-Recognition is an open-source project that implements a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) using TensorFlow to classify human activities from smartphone sensor data. It solves the problem of automatically recognizing movements like walking, sitting, and standing using raw accelerometer and gyroscope signals with minimal preprocessing.
Machine learning practitioners, researchers, and students interested in time-series classification, human activity recognition, or applying LSTM networks to sensor data. It's suitable for those seeking a practical deep learning example with real-world datasets.
Developers choose this project for its clear, end-to-end implementation that demonstrates how LSTMs can effectively model sequential sensor data without complex feature engineering. It provides a reproducible baseline with high accuracy and educational visualizations.
Overview
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
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