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 about a year ago)

Stargazers 3,292 (0 this week)

Watchers 159 (0 this week)

Forks 931

License mit

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RepositoryStats indexes 523,840 repositories, of these guillaume-chevalier/LSTM-Human-Activity-Recognition is ranked #14,104 (97th percentile) for total stargazers, and #9,492 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #316/14,471.

guillaume-chevalier/LSTM-Human-Activity-Recognition is also tagged with popular topics, for these it's ranked: deep-learning (#455/7720), machine-learning (#478/7264), tensorflow (#152/2146), neural-network (#83/1021)

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: 2024-05-31 @ 07:34pm, id: 59073471 / R_kgDOA4Vjvw