guillaume-chevalier/LSTM-Human-Activity-Recognition resource

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3482 stars · 935 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Jupyter Notebook tutorial demonstrating Human Activity Recognition using an LSTM recurrent neural network in TensorFlow on the UCI smartphone sensor dataset. It classifies six movement activities (walking, stairs, sitting, standing, laying) from accelerometer and gyroscope time-series data.

Use cases

• learn how to build an LSTM in TensorFlow for time-series classification
• classify smartphone sensor data into human activities
• example of RNN applied to wearable sensor signals
• tutorial on human activity recognition with deep learning
• starter code for sequence classification with LSTMs

When to choose

• you want a worked, educational example of LSTM-based sequence classification
• you are working with the UCI HAR smartphone sensor dataset
• you want to learn TensorFlow RNN APIs through a hands-on notebook

When to avoid

• you need a production-ready activity recognition system
• you need support for modern TensorFlow 2.x or PyTorch out of the box
• you need a maintained library with an API rather than a tutorial notebook

Facets

learning-resource · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python lstm rnn tensorflow human-activity-recognition tutorial jupyter-notebook time-series sensors

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