Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語


 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.

MYU Research

(proofreading and recording)

(translation service)

The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 32, Number 9(2) (2020)
Copyright(C) MYU K.K.
pp. 2999-3017
S&M2318 Research Paper of Special Issue
Published: September 18, 2020

Personalizing Activity Recognition Models by Selecting Compatible Classifiers with a Little Help from the User [PDF]

Trang Thuy Vu and Kaori Fujinami

(Received May 2, 2020; Accepted August 11, 2020)

Keywords: human activity recognition, machine learning, wearable sensors, personalization

In daily life, people perform activities every moment differently from one another. Thus, it is necessary to develop a robust system that can recognize human activities and cope with their individual differences. In this article, we propose a new method of individualizing a classifier by choosing the most suitable one based on the estimation of compatibility with a set of classifiers, which we call compatibility-based classifier personalization (CbCP). To make CbCP effective and reduce the burden on the user, the number of activities that a user needs to perform to provide data should be as small as possible. We propose two methods of ranking activities that are as effective in estimating the compatibility as using all activities: difference-based and correlation-based approaches. Additionally, we evaluated four methods of handling a case when more than two classifiers have the same level of compatibility, i.e., multi-compatible classifier handling, random choice, average compatibility reference, and ensemble classification with and without weighting. An offline experiment was carried out using two public datasets, i.e., Physical Activity Monitoring for Aging People 2 (PAMAP2) and Daily Life Activities (DaLiAc), to understand the characteristics of these methods. The results showed that the correlation-based method for activity ranking and the average compatibility reference for multi-compatible classifier handling are the best combination in terms of classification performance, the burden on the user, and computational complexity.

Corresponding author: Kaori Fujinami

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Trang Thuy Vu and Kaori Fujinami, Personalizing Activity Recognition Models by Selecting Compatible Classifiers with a Little Help from the User, Sens. Mater., Vol. 32, No. 9, 2020, p. 2999-3017.

Forthcoming Regular Issues

Forthcoming Special Issues

Special Issue on International Conference on BioSensors, BioElectronics, BioMedical Devices, BioMEMS/NEMS and Applications 2019 (Bio4Apps 2019) (1)
Guest editor, Hirofumi Nogami and Masaya Miyazaki (Kyushu University)
Conference website

Special Issue on Optical Sensors: Novel Materials, Approaches, and Applications
Guest editor, Yap Wing Fen (Universiti Putra Malaysia)

Special Issue on Intelligent Sensing Control Analysis, Optimization, and Automation (2)
Guest editor, Cheng-Chi Wang (National Chin-Yi University of Technology)

Special Issue on Geomatics Technologies for the Realization of Smart Cities (2)
Guest editor, He Huang and XiangLei Liu (Beijing University of Civil Engineering and Architecture)

Special Issue on Cyber–Physical Systems (CPS) and Internet of Things (IoT)
Guest editor, Yutaka Arakawa (Kyushu University)

Special Issue on Sensors and Materials Emerging Investigators in Japan
Guest editor, Tsuyoshi Minami (The University of Tokyo)

Copyright(C) MYU K.K. All Rights Reserved.