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GLSTM-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
Popularity
Repository stars3,4906,390Highest
7-day net stars——
Fork9283,715Highest
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2022-11-062024-04-17
Maintenance notesNot verifiedNot verified
Repository created2016-05-182015-01-25
License & use
LicenseMIT · PermissiveLicense not declared
PricingPricing not verifiedPricing not verified
Basics
Browse by purposeDatabases & dataDatabases & data
TypeDatasets & evaluationsDatasets & evaluations
SubcategoryText datasetsText datasets
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Check current official software, hosting, model and infrastructure pricing.
Pricing source——
First collected2026-11-032026-11-03
MetricRepository stars, not unique users or individual-skill installsRepository stars, not unique users or individual-skill installs
LanguageJupyter NotebookJupyter Notebook
About this projectHuman Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume ChevalierCOCO API - Dataset @ http://cocodataset.org/
Measured at2026-11-03 09:41 UTC2026-11-03 08:29 UTC
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