| Project/Area Number |
19K14589
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 12040:Applied mathematics and statistics-related
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| Research Institution | Tokyo City University (2023)
Tokyo University of Science (2019-2022) |
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Principal Investigator |
スヴィリドヴァ ニーナ 東京都市大学, 情報工学部, 講師 (70782829)
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| Project Period (FY) |
2019-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2022: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | photoplethysmogram / remote sensing / nonlinear dynamics / time series analysis / chaos / chaos theory / recurrence plot / Photoplethysmogram / deterministinc chaos / filtering / image processing / biomedical signal |
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| Outline of Research at the Start |
Photoplethysmogram (PPG) is widely used for health monitoring. Recent studies demonstrated that imaging PPG can be obtained from the conventional camera's video. It has great potential for health monitoring applications, but noise is a significant problem. Common filtration methods decrease PPG dynamics complexity. PPG dynamical characteristics can be used for health monitoring, so it is essential to preserve signal dynamics. This study aims to develop dynamics preserving method for coping with noise issue for data extraction from video recordings obtained from a conventional camera.
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| Outline of Annual Research Achievements |
In Fiscal Year 2023, the main focus was on analyzing various aspects of imaging photoplethysmogram (iPPG) dynamics and its dependence on the data recording protocol. Close attention was paid to dynamics characteristics, such as determinism, predictability, complexity, etc., changes in the iPPG signals recorded by conventional mobile devices such as tablets and smartphones. During this fiscal year, various video datasets were recorded by the iPad with camera specifications similar to the majority of modern smartphones. Additionally, reference photoplethysmogram (PPG) signals were recorded simultaneously. Time series data were extracted from the videos, and their dynamical characteristics were thoughtfully analyzed by the methods of nonlinear time series analysis and compared to those of conventional PPG signals recorded by specialized pulse oximeters.
Results confirmed that it is possible to record iPPG signals using a mobile device camera with dynamical characteristics compatible with those of the PPGs. Thus, depending on the recording protocol, iPPG data preserving essential dynamical characteristics, i.e. determinism, trajectory divergence, and predictability, were obtained, which is an essential result for applications in the area of daily health monitoring.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Numerous datasets were collected and analyzed. The obtained results demonstrated the possibility of recording signals comparable to PPGs from pulse oximeters by conventional mobile device cameras. The results were presented at academic conferences.
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| Strategy for Future Research Activity |
Numerous PPG and iPPG data sets were collected and analyzed in fiscal year 2023. In the fiscal year 2024, further analysis of collected datasets will be performed, and additional datasets will be collected to achieve better statistical significance of the results. The results obtained so far, and the further analysis results will be presented in academic conferences and published as a research paper.
If challenges are encountered during further data analysis or results interpretation, a second opinion and advice will be sought from the faculty members of Tokyo City University, who specialize in various fields related to signal processing and statistical analysis.
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