Project/Area Number |
20K12080
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 62020:Web informatics and service informatics-related
|
Research Institution | The University of Aizu |
Principal Investigator |
MARKOV K 会津大学, コンピュータ理工学部, 教授 (80394998)
|
Co-Investigator(Kenkyū-buntansha) |
松井 知子 統計数理研究所, モデリング研究系, 教授 (10370090)
齋藤 純平 福島県立医科大学, 医学部, 講師 (50332929)
|
Project Period (FY) |
2020-04-01 – 2024-03-31
|
Project Status |
Granted (Fiscal Year 2022)
|
Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
|
Keywords | cough recognition / cough monitoring / DNN audio analysis / machine learning / deep learning |
Outline of Research at the Start |
現在,我々の知る限り咳嗽の種類(痰がらみの咳嗽,痰を伴わない咳嗽)ごとに咳嗽頻度を抽出する完全な自動咳監視サービスシステムは存在しない。本研究において、世界に先駆けて本システムを開発する。本研究成果は、最先端の統計・機械学習技術に基づくシステムとしての科学的価値と独創性を持つだけでなく、実用上容易に(商業的に)複製することができるため、 病院や一般開業医に提供できる。本モニタリングシステムが日常診療に導入できれば、本邦の呼吸器疾患で最も多い症状である咳嗽の簡便かつ迅速な診断が可能なるだけではなく、客観的かつ正確な治療効果が判定も行えるため、患者にあった個別化医療を提供することができると考える.
|
Outline of Annual Research Achievements |
During this year we built a high performance cough detection and monitoring system and successfully evaluated it using the data collected at FMU. There are 11 patients audio recordings in the dataset with highly irregular cough events time distribution. Out approach is to segment the input data into 10sec long segments and process each segment separately. Out model is based on fine tuned large audio model called HuBERT which can identify cough frames with high accuracy. In addition, we trained a special network which estimates the probability of each identified cough frame being the first, second, etc, in the couch event. This way, we can distinguish separate cough events even when they come right one after another within a long sequence of cough frames.
|
Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
Currently, we are conducting experiments involving other openly available methods and models for cough event detection and monitoring in order to compare the performance of our system and the other state-of-the-art systems. To achieve statistically significant experimental results, we are performing a 4-fold cross validation experiments and so far our system has obtained more than 90% F1 score.
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Strategy for Future Research Activity |
After finishing the experiments, we plan to summarize the achievements of this project and publish them in a journal paper.
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