[image 03823] Re: 講演会のお知らせ: Prof. Thomas Plageman at University of Oslo

[Shin'ichi Satoh]

image-mlの皆様、

たびたびすみません。開催が来週に迫りましたので、再度ご案内申し上げます。
どうぞふるってご参加ください。

-- Shin'ichi


> image-mlの皆様、
> 
> 国立情報学研究所の佐藤真一です。オスロ大学のThomas Plageman教授による
> 以下の講演会のお知らせをお送りいたします。Springerのジャーナルの
> Multimedia Systemsの編集委員長も務めておられ、マルチメディア分野の重鎮
> です。トークはIoTや機械学習を用いた睡眠時無呼吸症候群に対する取り組み
> の話であり、実際われわれも無関係ではないかもしれません。
> 
> 参加無料、参加登録も不要です。どうぞご参加下さい。
> 
> -- Shin'ichi
> 
> 日時: 2019年12月16日(月) 17:00-18:00
> 場所: 国立情報学研究所 15階 1512会議室
> http://www.nii.ac.jp/access/
> 
> Title: Towards Sleep Apnea Detection with Consumer Electronics and Machine Learning
> 
> Speaker: Prof. Thomas Plageman, University of Oslo
> 
> Abstract:
> 
> Obstructive sleep apnea (OSA) is a common, but severely
> under-diagnosed sleep disorder that affects the natural breathing
> cycle during sleep with the periods of reduced respiration or no
> airflow at all. It is the long-term goal of the Cesar project (RCN,
> FriPro) to increase the percentage of diagnosed OSA cases, reduce the
> time to diagnosis, and support long term monitoring of patients with
> user friendly and cost-efficient tools for sleep analysis at
> home. Core elements are mobile computing platforms (e.g.,
> smartphones), consumer electronics sensors, and machine learning (ML)
> for OSA detection.
> 
> In this presentation, we will give some background on OSA and discuss
> some of the studies we have performed in the project. Using public
> data sets from physionet.org<http://physionet.org> we could show that 
> “simple” ML techniques could achieve rather high classification
> performance even for only one signal using a high-quality data
> set. Under lab conditions we could show that some of the available
> low-cost breathing sensors produce good data – compared to the gold
> standard for unattended sleep monitoring at home. Due to the
> collaboration with the ongoing A3 study at the Oslo University
> Hospital we could use one of these low-cost sensors for sleep
> monitoring of 50 patients at home. The resulting data is in its raw
> format of low quality and required thorough
> preprocessing. Furthermore, we discuss how data augmentation with
> Generative Adversarial Networks can be used to increase, re-balance
> and personalize data sets. We will conclude the presentation with a
> discussion about the future technical challenges as well as some
> challenges that need to be addressed by law and ethics.
> 
> 
> Short Bio: 
> 
> Thomas Plageman received the Dr. Sc. degree in computer science from
> the Swiss Federal Institute of Technology (ETH), Zurich, Switzerland,
> in 1994, with the Medal of ETH Zurich in 1995. He has been a Professor
> with the University of Oslo, Oslo, Norway, since 1996. He currently
> leads the Research Group in Distributed Multimedia Systems, Department
> of Informatics, University of Oslo. He has published over 200 papers
> in peer-reviewed journals, conferences, and workshops in his
> field. His research interests include protocol architectures and
> middleware solutions for multimedia communication and mobile systems,
> future Internet, and multimodal sensor systems, complex event
> processing, and machine learning with physiological time-series
> data. He is a member of the Association for Computing Machinery
> (ACM). He serves as Associate Editor for the ACM Transactions on
> Multimedia Computing, Communication, and Applications and
> Editor-in-Chief for Multimedia Systems (Springer).