Code Related to the Roman paper: "Automatic detection of human interactions from RGB-D data for social activity classification"

Comparison of classifiers on Human Activity Recognition

Activity recognition by phone IMU with Java implemented

An Android Activity Recognition app, able to catch action as run, walking, standing, sweeping, vacooming etc.. Read the wiki for other info

Fitness tracker che sfrutta sensori inerziali

Physical activity recognition with deep learning models.

🏃2023 spring Mobile Health and Activity Monitoring

Data analysis in the domains of time and frequency, using signals collected from mobile phone accelerometers in order to classify/identify the activity performed, corresponding to 12 different movements (UC, 2020)

Machine learning experiment replications

Musix Sleep - Android application for Music hackathon

Projekt zrealizowałem w trakcie studiów w ramach kursu: programowanie systemów mobilnych. Celem projektu było stworzenie aplikacji mobilnej rozpoznającej wykonywaną aktywność fizyczną na podstawie danych z akcelerometru.

Personal blog, based on github pages.

activity_recognition_flutter 4.0.3 location 4.3.0 latlong 0.6.1

High definition (HD) video cameras have been used to record daily activities at construction jobsites into videos. These videos contain rich workforce and equipment information for site engineers and project managers to analyze construction productivity, monitor construction progress, inspect jobsite safety, etc.

Grad project of which the system uses smartwatch accelerometer data to detect dementia patient’s 4 basic activities (sit, sleep, stand, and walk) during the day.

Pandemic Watch System Server API'S

This project involves a comprehensive comparative analysis of various machine learning models to classify activities based on a given dataset. The analysis follows a structured approach, including data exploration, model training, model evaluation, and results interpretation to identify the best performing model.

Continuous activity recognition metrics based on (Ward et al, 2011)