Naslov
Physical Activity Recognition Based on Machine Learning

Autori
Jurčić, Krunoslav ; Magjarević, Ratko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of tThe 29th Minisymposium of the Department of Measurement and Information Systems, Budapest University of Technology and Economics (BME), Faculty of Electrical Engineering and Informatics (VIK) / Renczes, Balazs - Budimpešta : Department of Measurement and Information Systems, Budapest University of Technology and Economics, 2022, 37-41

ISBN
978-963-421-872-2

Skup
29th Minisymposium of the Department of Measurement and Information Systems (BME)

Mjesto i datum
Budimpešta, Mađarska, 07.02.2022. - 08.02.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
biomedical engineering, physical activity, machine learning, signal processing, accelerometer, magnetometer, gyroscope, fall detection, fall distinction, activities of daily living

Sažetak
The following paper presents a comparison study of various machine learning techniques in recognition of activities of daily living (ADL), with special attention being given to movements during human falling and the distinction among various types of falls. The motivation for the development of physical activity recognition algorithm includes keeping track of users’ activities in real-time, and possible diagnostics of unwanted and unexpected movements and/or events. The activities recorded and processed in this study include various types of daily activities, such as walking, running, etc., while fall activities include falling forward, falling backward, falling left and right (front fall, back fall and side fall). The algorithm was trained on two publicly available datasets containing signals from an accelerometer, a magnetometer and a gyroscope.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti

POVEZANOST RADA