Young Researcher Paper Award 2023

Notice of retraction
Vol. 34, No. 8(3), S&M3042

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

Print: ISSN 0914-4935
Online: ISSN 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 34, Number 8(1) (2022)
Copyright(C) MYU K.K.
pp. 2911-2928
S&M3014 Research Paper of Special Issue
Published: August 2, 2022

Inertial Measurement Unit-sensor-based Short Stick Exercise Tracking to Improve Health of Elderly People [PDF]

Kazuki Oi, Yugo Nakamura, Yuki Matsuda, Manato Fujimoto, and Keiichi Yasumoto

(Received May 11, 2022; Accepted July 5, 2022)

Keywords: short stick exercise, machine learning, IMU

Short stick exercises have been attracting attention from the viewpoint of preventing falls and improving the health of elderly people and are generally performed under the guidance of instructors and nursing staff at nursing homes. However, in situations such as the COVID-19 pandemic, where people should refrain from unnecessary outings, it is advisable that individuals perform short stick exercises at home and record their exercise implementation status. In this paper, we propose an inertial measurement unit (IMU)-sensor-based short stick exercise tracking method that can automatically record the types and amounts of exercises performed using a short stick equipped with an IMU sensor. The proposed method extracts time-domain and frequency-domain features from linear acceleration and quaternion time-series data obtained from the IMU sensor and classifies the type of exercise using an inference model based on machine learning algorithms. To evaluate the proposed method, we collected sensor data from 21 young subjects (in their 20s) and 14 elderly subjects (79–95 years old), where the participants performed three sets (10 times per set) of eight basic types of short stick exercises (five types for elderly people). As a result of evaluating the proposed method using this data set, we confirmed that when LightGBM was used as the learning algorithm, it achieved F values of 90.0% and 86.6% for recognizing the type of exercise for young and elderly people, respectively.

Corresponding author: Yugo Nakamura

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

Cite this article
Kazuki Oi, Yugo Nakamura, Yuki Matsuda, Manato Fujimoto, and Keiichi Yasumoto, Inertial Measurement Unit-sensor-based Short Stick Exercise Tracking to Improve Health of Elderly People, Sens. Mater., Vol. 34, No. 8, 2022, p. 2911-2928.

Forthcoming Regular Issues

Forthcoming Special Issues

Applications of Novel Sensors and Related Technologies for Internet of Things
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper

Special Issue on Advanced Data Sensing and Processing Technologies for Smart Community and Smart Life
Guest editor, Tatsuya Yamazaki (Niigata University)
Call for paper

Special Issue on Advanced Sensing Technologies and Their Applications in Human/Animal Activity Recognition and Behavior Understanding
Guest editor, Kaori Fujinami (Tokyo University of Agriculture and Technology)
Call for paper

Special Issue on International Conference on Biosensors, Bioelectronics, Biomedical Devices, BioMEMS/NEMS and Applications 2023 (Bio4Apps 2023)
Guest editor, Dzung Viet Dao (Griffith University) and Cong Thanh Nguyen (Griffith University)
Conference website
Call for paper

Special Issue on Piezoelectric Thin Films and Piezoelectric MEMS
Guest editor, Isaku Kanno (Kobe University)
Call for paper

Special Issue on Advanced Micro/Nanomaterials for Various Sensor Applications (Selected Papers from ICASI 2023)
Guest editor, Sheng-Joue Young (National United University)
Conference website
Call for paper

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