There are 57 repositories under human-activity-recognition topic.
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
Real-Time Spatio-Temporally Localized Activity Detection by Tracking Body Keypoints
Convolutional Neural Network for Human Activity Recognition in Tensorflow
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope) (PMC Journal) (IoTDI'19)
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.
Unity's privacy-preserving human-centric synthetic data generator
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.
Quickly add MediaPipe Pose Estimation and Detection to your iOS app. Enable powerful features in your app powered by the body or hand.
[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
Abnormal Human Behaviors Detection/ Road Accident Detection From Surveillance Videos/ Real-World Anomaly Detection in Surveillance Videos/ C3D Feature Extraction
Multi Person Skeleton Based Action Recognition and Tracking
Human Activity Recognition based on WiFi Channel State Information
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.
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 using Channel State Information
Self-supervised learning for wearables using the UK-Biobank (>700,000 person-days)
Implementation of Action Recognition using 3D Convnet on UCF-101 dataset.
Surveillance Perspective Human Action Recognition Dataset: 7759 Videos from 14 Action Classes, aggregated from multiple sources, all cropped spatio-temporally and filmed from a surveillance-camera like position.
Contrastive Learning (SimCLR) for Human Activity Recognition
Activity Assistant provides a platform for logging, evaluating and predicting Activities of Daily Living for Home Assistant.
Keras implementation of CNN, DeepConvLSTM, and SDAE and LightGBM for sensor-based Human Activity Recognition (HAR).
Transformer for Human Activity Recognition
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
Use a LSTM network to predict human activities from sensor signals collected from a smartphone
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')
DANA: Dimension-Adaptive Neural Architecture (UbiComp'21)( ACM IMWUT)
[ECAI 2020] Tensorflow 2.x Implementation of "Human Activity Recognition from Wearable Sensor Data Using Self-Attention"
This is a platform containing the datasets and federated learning algorithms in IoT environments.
Official source code for "Continual 3D Convolutional Neural Networks for Real-time Processing of Videos" [ECCV2022]