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

Python implementation of KNN and DTW classification algorithm

Convolutional Neural Network for Human Activity Recognition in Tensorflow

custom human activity recognition modules by pose estimation and cascaded inference using sklearn API

Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)

Human Trajectory Prediction Dataset Benchmark

Implementation of Action Recognition using 3D Convnet on UCF-101 dataset.

Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.

Abnormal Human Behaviors Detection/ Road Accident Detection From Surveillance Videos/ Real-World Anomaly Detection in Surveillance Videos/ C3D Feature Extraction

Kernel Cross-Correlator (KCC) for Tracking and Recognition (AAAI 2018)

Human activity recognition on iOS using Swift, CoreMotion, and NeuralNet.

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.

Tensorflow Implementation of ECCV'18 paper: Multimodal Human Motion Synthesis

Use a LSTM network to predict human activities from sensor signals collected from a smartphone

This is a data-set for Human Activities & Gestures Recognition (HAGR) using the Channel State information (CSI) of IEEE 802.11n devices

Recognizing human activities using Deep Learning

Code for paper: Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition

LSTM based human activity recognition using smart phone sensor dataset

Efficient LSTM parallelization on smartphone GPU

AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors

A new and computationally cheap method to perform human activity recognition using PoseNet and LSTM. Where we use PoseNet for Preprocessing and LSTM for understand the sequence.

Human Activity Recognition Using Deep Neural Network

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

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).

Human Activity Recognition on the Wireless Sensor Data Mining (WISDM) dataset using LSTM Recurrent Neural Networks

Human activity recognition using human pose information to aid classification.

Deep learning and LSTM approaches for human activity recognition

Offering recent HAR papers collection

Activity Recognition using Temporal Optical Flow Convolutional Features and Multi-Layer LSTM