human-activity-recognition

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

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

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

Convolutional Neural Network for Human Activity Recognition in Tensorflow

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) (PMC Journal) (IoTDI'19)

Unity's privacy-preserving human-centric synthetic data generator

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

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

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.

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

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.

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

Human Activity Recognition using Channel State Information

Activity assistant provides a platform for logging and predicting Activities of Daily Living (ADLs) in realtime for home assistant users.

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

Keras implementation of CNN, DeepConvLSTM, and SDAE and LightGBM for sensor-based Human Activity Recognition (HAR).

An up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources. Please note that most of the collections of researches are mainly based on IMU data.

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

Multi Person Skeleton Based Action Recognition and Tracking

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

Recognizing human activities using Deep Learning

Human Activity Recognition (HAR) with Keras and CoreML

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

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

Multimodal human activity recognition using wrist-worn wearable sensors.

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

Official GitHub page of the best-paper award publication "Improving Deep Learning for HAR with shallow LSTMs" presented at the International Symposium on Wearable Computers 21' (ISWC 21')