This repo is to investigate Neural Networks for recognition of human activities.

A WiFi Sensing based Yoga Pose Recognition using Machine Learning Techniques

An late fusion architecture between I3D and Xception for Human Activity Recognition

AN ADVANCED APPROACH TO RECOGNIZE HUMAN ACTIVITIES VIA DEEP LEARNING

Human Activity Recogition GUI

NTU SC1015 SC16 Team 2 Project

Activity Recognition from videos using keypoints detection

"Embark on a cutting-edge journey in Human Activity Recognition using a fusion of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This project includes model training, metric visualization, and action prediction in videos. Experience seamless interaction with a Streamlit-powered user-friendly version (at the bottom)

Identification and Prediction of Activities in the Weight Lifting Exercises

ActionSense is an innovative project that combines the power of computer vision with the connectivity of IoT to create a seamless human activity recognition system. Using OpenCV for accurate motion detection and pySerial for IoT integration, ActionSense transforms how environments respond to human actions, enhancing automation and interaction.

This repo. for code and data set fro different human activities and Gait analysis research https://sites.google.com/view/vsemwal/research/human-activities-gait-data-set

For HAR, our novel HDAD (IGAV) dataset was constructed by performing 4 dynamic and 3 static activities with the accelerometer and gyroscope sensors of the IOS smart phone in two different positions for a total of 15 seconds. Mentioned activities were collected in real time by placing them on the waist of a total of 10 volunteers.

This repository contains the core methods and models described in the paper “Represent Code as Action Sequence for Predicting Next Method Call.” It uses action sequence modeling to predict method calls in Python code based on developer intentions, treating code editing as a sequence of human-like actions.