Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Accurate physical activity recognition using multidimensional features and markov model for smart health fitnessopen access

Authors
Nadeem, AmirJalal, AhmadKim, Kibum
Issue Date
Nov-2020
Publisher
MDPI AG
Keywords
Body parts detection; Markov model; Physical activity recognition; Spatiotemporal features
Citation
Symmetry, v.12, no.11, pp 1 - 17
Pages
17
Indexed
SCIE
SCOPUS
Journal Title
Symmetry
Volume
12
Number
11
Start Page
1
End Page
17
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1838
DOI
10.3390/sym12111766
ISSN
2073-8994
2073-8994
Abstract
Recent developments in sensor technologies enable physical activity recognition (PAR) as an essential tool for smart health monitoring and for fitness exercises. For efficient PAR, model representation and training are significant factors contributing to the ultimate success of recognition systems because model representation and accurate detection of body parts and physical activities cannot be distinguished if the system is not well trained. This paper provides a unified framework that explores multidimensional features with the help of a fusion of body part models and quadratic discriminant analysis which uses these features for markerless human pose estimation. Multilevel features are extracted as displacement parameters to work as spatiotemporal properties. These properties represent the respective positions of the body parts with respect to time. Finally, these features are processed by a maximum entropy Markov model as a recognition engine based on transition and emission probability values. Experimental results demonstrate that the proposed model produces more accurate results compared to the state-of-the-art methods for both body part detection and for physical activity recognition. The accuracy of the proposed method for body part detection is 90.91% on a University of Central Florida’s (UCF) sports action dataset and, for activity recognition on a UCF YouTube action dataset and an IM-Daily RGB Events dataset, accuracy is 89.09% and 88.26% respectively. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Files in This Item
Appears in
Collections
COLLEGE OF COMPUTING > SCHOOL OF MEDIA, CULTURE, AND DESIGN TECHNOLOGY > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kim, Kibum photo

Kim, Kibum
COLLEGE OF COMPUTING (SCHOOL OF MEDIA, CULTURE, AND DESIGN TECHNOLOGY)
Read more

Altmetrics

Total Views & Downloads

BROWSE