Human character recognition project

This project classifies different human activities into their respective actions using the `LibSVM` library.

ROS/ROS2 -- Navigation, Manipulation, Mimicking, Sensor Fusion, VR, Speech Recogition, Activity Recognition, Computer Vision

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Using random forest to recognize human activity.

The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.

Searching Efficient Models for Human Activity Recognition

Multi-view balance related body landmark (joints) dataset with synchronized center of pressure (CoP)

Lo scopo dell'applicazione è quello di costruire un feature vector per la predizione dei pasti in base a valori registrati da sensori di qualità dell'aria. Le feature prodotte verranno automaticamente unite a feature statistiche già presenti in alcuni files. Per la classificazione utilizzare il Knowledge Flow Enviroment di Weka impostato nel fi…

In this project, our prime focus was to predict human activity by using the smartphone’s sensors. The tasks associated with this project were the classification and clustering of humans on the basis of their activities.

Human Pose Tracking-Skeleton Generation

Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket.

Report task for Neurocognitive Computing | Winter Semester 2022-23

LSTM based architecture to classify Human activity based on sensor data.

This project represents the implementation of the Spatio-Temporal Image Encoding used in the paper "Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection" published in the "International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications(VISAPP) 2022".

Human Activity Recognition Using Smartphones Dataset

In this project, we try to track the physical activities of people through sensors from smartphones placed in different positions of the body.

Self-Explainable Zero-shot Human Activity Recognition Network

Human Activity Detection with TensorFlow and Python.