Physical Activity Recognition Using Posterior-Adapted Class-Based Fusion of Multiaccelerometer Data

Loading...
Thumbnail Image
File version

Accepted Manuscript (AM)

Author(s)
Chowdhury, Alok Kumar
Tjondronegoro, Dian
Chandran, Vinod
Trost, Stewart G
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2018
Size
File type(s)
Location
License
Abstract

This paper proposes the use of posterior-adapted class-based weighted decision fusion to effectively combine multiple accelerometer data for improving physical activity recognition. The cutting-edge performance of this method is benchmarked against model-based weighted fusion and class-based weighted fusion without posterior adaptation, based on two publicly available datasets, namely PAMAP2 and MHEALTH. Experimental results show that: 1) posterior-adapted class-based weighted fusion outperformed model-based and class-based weighted fusion; 2) decision fusion with two accelerometers showed statistically significant improvement in average performance compared to the use of a single accelerometer; 3) generally, decision fusion from three accelerometers did not show further improvement from the best combination of two accelerometers; and 4) a combination of ankle and wrist located accelerometers showed the best overall performance compared to any combination of two or three accelerometers.

Journal Title

IEEE Journal of Biomedical and Health Informatics

Conference Title
Book Title
Edition
Volume

22

Issue

3

Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Item Access Status
Note
Access the data
Related item(s)
Subject

Biomedical and clinical sciences

Health informatics and information systems

Science & Technology

Technology

Life Sciences & Biomedicine

Computer Science, Information Systems

Computer Science, Interdisciplinary Applications

Persistent link to this record
Citation

Chowdhury, AK; Tjondronegoro, D; Chandran, V; Trost, SG, Physical Activity Recognition Using Posterior-Adapted Class-Based Fusion of Multiaccelerometer Data, IEEE Journal of Biomedical and Health Informatics, 2018, 22 (3), pp. 678-685

Collections