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Impact of Sensor-Axis Combinations on Machine Learning Accuracy for Human Activity Recognition Using Accelerometer Data in Clinical Settings Cover

Impact of Sensor-Axis Combinations on Machine Learning Accuracy for Human Activity Recognition Using Accelerometer Data in Clinical Settings

Physical Activity and Health
Volume 9 (2025): Issue 1
By: Takahiro Yamane,  Moeka Kimura and  Mizuki Morita  
Open Access
|May 2025

Authors

Takahiro Yamane

pwf454co@s.okayama-u.ac.jp

Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama

Moeka Kimura

p7zt0ndt@s.okayama-u.ac.jp

Faculty of Health Sciences, Okayama University Medical School, Okayama

Mizuki Morita

mizuki@okayama-u.ac.jp

Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama; Faculty of Health Sciences, Okayama University Medical School, Okayama
DOI: https://doi.org/10.5334/paah.441 | Journal eISSN: 2515-2270
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Language: English
Submitted on: Feb 12, 2025
|
Accepted on: Apr 21, 2025
|
Published on: May 9, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
wearable devices,
machine learning,
human activity recognition,
sensor-axis combinations,
chronic obstructive pulmonary disease,
electrocardiography

© 2025 Takahiro Yamane, Moeka Kimura, Mizuki Morita, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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