Inhalation Motion Analysis and Visualization of Error Areas Using Two Inertial Measurement Unit Sensors
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.
Authors
- Tomoyuki Shimono (ORCID: https://orcid.org/0000-0002-0115-5499)
- Hideo Nakada
- Atsushi Hasegawa (ORCID: https://orcid.org/0000-0003-4989-5610)
- Shunya Takano (ORCID: https://orcid.org/0000-0002-5795-1005)
- Katsunori Masaki (ORCID: https://orcid.org/0000-0003-0909-9409)
- Hiroki Kabata (ORCID: https://orcid.org/0000-0001-8853-6149)
- Jun Miyata (ORCID: https://orcid.org/0000-0002-3189-1702)
- Koichi Fukunaga (ORCID: https://orcid.org/0000-0001-7460-1990)
- Jun Hakamata (ORCID: https://orcid.org/0000-0001-6000-1775)
Institutions
- Kanagawa Institute of Technology (JP)
- Keio University (JP)
- Keio University Hospital (JP)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3617064
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00