Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Lstm Human Activity Recognition | 3,074 | a year ago | 19 | mit | Jupyter Notebook | |||||
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 | ||||||||||
Sense | 729 | 2 years ago | n,ull | mit | Python | |||||
Enhance your application with the ability to see and interact with humans using any RGB camera. | ||||||||||
Human Activity Recognition Using Cnn | 355 | 4 years ago | apache-2.0 | Jupyter Notebook | ||||||
Convolutional Neural Network for Human Activity Recognition in Tensorflow | ||||||||||
Wearablesensordata | 28 | 5 years ago | Python | |||||||
This repository provides the codes and data used in our paper "Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art", where we implement and evaluate several state-of-the-art approaches, ranging from handcrafted-based methods to convolutional neural networks. | ||||||||||
C4a_behavior_recognition | 18 | 5 years ago | Python | |||||||
Behavior recognition for the City4Age project | ||||||||||
Dana | 16 | 3 years ago | 2 | mit | Jupyter Notebook | |||||
DANA: Dimension-Adaptive Neural Architecture (UbiComp'21)( ACM IMWUT) | ||||||||||
Auritus | 16 | 6 months ago | 1 | bsd-3-clause | C++ | |||||
Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables | ||||||||||
Human Activity Dih18 | 15 | 2 years ago | 9 | Python | ||||||
Human Activity Recognition Using Deep Neural Network | ||||||||||
Cnnhar | 14 | 6 years ago | Matlab | |||||||
Deep convolutional neural network for human activity recognition | ||||||||||
Human Activity Recognition | 13 | 6 years ago | mit | Jupyter Notebook | ||||||
Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING). |