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Inhalation Motion Analysis and Visualization of Error Areas Using Two Inertial Measurement Unit Sensors
Author(s) -
Shunya Takano,
Atsushi Hasegawa,
Tomoyuki Shimono,
Katsunori Masaki,
Hideo Nakada,
Jun Hakamata,
Hiroki Kabata,
Jun Miyata,
Koichi Fukunaga
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3617064
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory diseases, and proper use of inhalers is crucial for effective symptom management. However, many patients use their inhalers incorrectly. This study proposes a method for evaluating inhaler-use behavior by employing inertial sensors to monitor patients with bronchial asthma or COPD. By augmenting an Ellipta™ inhaler with inertial measurement units, this study evaluates patient inhalation motions using the acquired motion data. Compared with conventional methods, the proposed method is less affected by external factors such as ambient sound and temperature and can be applied outside clinical settings. The evaluation process uses a linear discriminant analysis algorithm to identify key characteristic variables associated with specific inhaler-use errors, and a judgment method using these variables is proposed. A dynamic programming matching algorithm is then applied to assess correctness. Experimental results indicate that the proposed method achieves high discriminant accuracy. By considering waveform similarity, it enables error visualization unlike contemporary methods. The proposed inhalation method and accompanying dataset offer valuable guidance for future research and practical feedback for patients.

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