| 研究実績の概要 |
We have built an open access heart sound database, i.e., heart sounds Shenzhen corpus (HSS1.0), which is the largest open access heart sound database collected from single medical centre. In addition, we made a benchmark work on using both the classic machine learning (ML) models and the state-of-the-art deep learning (DL) models. A pilot study based on HSS1.0 by using wavelet analysis combining with deep recurrent neural networks (RNNs) were presented. We also investigated the capacity of ML and/or DL for new tasks in other interdisciplinary fields including robotic control, materials science, security surveillance, ecology, and smart grid. A perspective study on using affective computing technologies to facilitate the mental health monitoring and therapy was published. We proposed a novel transfer learning models pre-trained by audio data for heart sound classification task, which was demonstrated to be superior to the representations extracted by models pre-trained by the widely used image data. We gave a brief opinion work on computer audition (CA) for healthcare applications. Moreover, we summarised and conducted a comprehensive review study on snore sound classification using CA methods. Based on the aforementioned studies, we found that: First, a standard open access database is the prerequisite for a sustainable research of artificial intelligence (AI) based healthcare applications. Second, we can collaborate more with researchers from other fields to commonly contribute to innovations.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
We have implemented an AI-enabled system for automatically analysing the elderly’s behaviour by their daily life usage data of the smart appliances, e.g., television or refrigerator. To the best of our knowledge, this is a first study on using smart appliances’ data to analyse the elderly’s behaviour. The results are encouraging to demonstrate the feasibility to use this framework to monitor the elderly’s symptom status when they are living alone. Motivated by the success of HSS1.0, we re-defined the task, and made modifications of the original audio recordings, to make a new database, i.e., HSS1.5. We are now working towards to release both HSS1.0 and HSS1.5 in the near future workshops and/or challenges. Then, we can make a fair comparison of the participants’ contributions, and summarise the state-of-the-art in current heart sound classification studies. In addition, we have a collaborative work with other AI experts on the multi-label learning algorithms. With an international collaboration between Japan, China, Germany, and UK, we have made a series pilot studies/reviews on COVID-19. We are now working on the development of an open source deep learning toolkit, i.e., deepSELF, which has a combination of the state-of-the-art deep learning, transfer learning, and end-to-end learning frameworks. By using this powerful toolkit, we have successfully beat the official baseline in the ComParE 2020 Challenge on the Mask Task. We also contribute two other collaborative papers on music and education AI based applications.
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| 今後の研究の推進方策 |
The current achievements of this research have already been much more than original plan. In future work, we will investigate more potential valuable fields. First, we will continuously investigate the ML and DL methods for analysis of some types of intensive longitudinal biomedical signals, e.g., spontaneous physical activity. We would like to understand some fundamental knowledge about the features and models for the classification and/or regression tasks. Second, we will make an intensive study on leveraging CA based methods for analysing the speech of COVID-19 patients. We are already collaborating with the colleagues from a background of medicine in Wuhan, China, and the AI researchers in Germany and UK. We would like to present a benchmark work of the database and a series of studies on using the state-of-the-art signal processing and ML methods for analysis of the database. We are also planning to organise several challenges and/or workshops on the topic of AI for fighting against the COVID-19. Third, we will develop another open source toolkit for data augmentation, which is essential to improve the generalisation of DL models, specifically, for data scarcity scenarios. Fourth, we will investigate the ML strategies, like semi-supervised learning, active learning, and cooperative learning, for their capacity in reducing the human annotation works. Fifth, we will start some specific AI based healthcare applications for elderly people who are living alone.
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