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

Date Created 2016-05-18 (8 years ago)

Commits 29 (last one 2 years ago)

Stargazers 3,350 (6 this week)

Watchers 160 (0 this week)

Forks 938

License mit

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RepositoryStats indexes 584,353 repositories, of these guillaume-chevalier/LSTM-Human-Activity-Recognition is ranked #14,796 (97th percentile) for total stargazers, and #9,518 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #346/17,098.

guillaume-chevalier/LSTM-Human-Activity-Recognition is also tagged with popular topics, for these it's ranked: deep-learning (#477/8389), machine-learning (#499/7926), tensorflow (#157/2241), neural-network (#86/1090)

Other Information

guillaume-chevalier/LSTM-Human-Activity-Recognition has 2 open pull requests on Github, 1 pull request has been merged over the lifetime of the repository.

Github issues are enabled, there are 20 open issues and 21 closed issues.

All Topics

rnn lstm tensorflow deep-learning neural-network machine-learning activity-recognition recurrent-neural-networks human-activity-recognition

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1 commits on the default branch (master) since jan '22

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updated: 2024-11-20 @ 04:21pm, id: 59073471 / R_kgDOA4Vjvw