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講演 - 井上　創造

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人工知能の社会実装に向けて

自動制御連合講演会講演論文集 2017年01月

Recognizing Unknown Activities Using Semantic Word Vectors and Twitter Timestamps

International Symposium on Applied Engineering and Sciences (SAES2016) 2016年12月 publications/389

Deep Recurrent Neural Network for Human Activity Recognition

International Symposium on Applied Engineering and Sciences (SAES2016) 2016年12月 publications/390

Nursing Activity Recognition and Future Prediction in Hospitals

International Symposium on Applied Engineering and Sciences (SAES2016) 2016年12月 publications/393

Text Mining for Nursing Care Service Improvement

International Symposium on Applied Engineering and Sciences (SAES2016) 2016年12月 publications/392

Robust Methods for Sensor-based Activity Recognition

International Symposium on Applied Engineering and Sciences (SAES2016) 2016年12月 publications/391

(Invited) Activity Recognition and Future Prediction in Hospitals

First International Workshop on Smart Sensing Systems (IWSSS’16) 2016年11月 publications/382

(Invited) Activity Recognition and Future Prediction in Hospitals

First International Workshop on Smart Sensing Systems (IWSSS’16) 2016年11月

開催期間： 2016年11月28日発表言語：英語講演種別：特別講演開催地：Hiroshima

In this paper, we firstly introduce our work [9] where we proposed for recognizing whole day activities using prior knowledge, and applied the method for real nursing sensor dataset we have col- lected. Then, we introduce the method for predicting near future of nurses by integrating nurse activity data, location data, and medical records, based on our work[10]. For both works, we independently collected real and open nursing datasets with 2 weeks of accelerom- eters and training labels from 22 nurses for the former work, and nurse activity, location, medical payment, and nursing needs data from 35 nurses and 96 patients for 40 days for the latter work.

Towards Smarter Life Care Using Sensing and Big Data Analytics

Kobe University Core-Team Workshop on Cyber-Physical System for Smarter World (CPS-SW 2016) 2016年03月 publications/349

モバイル行動認識の研究動向

電子情報通信学会総合大会企画セッション（都市と市民のためのCPS（サイバーフィジカルシステム）の姿を描く） 2016年03月 publications/337

介護サービス向上のためのテキスト分析とセンシング実験について

人間行動センシングミーティング2015 2016年01月 publications/338

Understanding Nursing Activities with Long-term Mobile Activity Recognition with Big Dataset

The 47th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS) 2015年12月 publications/323

Understanding Nursing Activities with Long-term Mobile Activity Recognition with Big Dataset

The 47th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS) 2015年12月

開催期間： 2015年12月05日発表言語：英語講演種別：特別講演開催地：Hawaii, USA

In this paper, we introduce a real nursing sensor dataset which includes labeled dataset for supervised machine learning and the big data combined with patient medical records and sensors attempted for 2 years, and also describe a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day and utilizing importance sampling and Bayesian estimation, based on our paper at UbiComp2015. Moreover, we demonstrate data mining by applying our method to the bigger data with additional hospital data. Our method of recognizing a whole day activities outperformed the traditional method without using the prior knowledge. Moreover, the method significantly reduces the duration errors of activity segments. We also demonstrate a data mining applying our method to bigger data in a hospital, and show several results about the correlations with nurse profiles and patients’ status.

(Keynote) Pervasive Sensing and Healthcare Applications

IIAE International Conference on Intelligent Systems and Image Processing 2015年09月

(Keynote) Pervasive Sensing and Healthcare Applications

IIAE International Conference on Intelligent Systems and Image Processing 2015年09月

開催期間： 2015年09月03日発表言語：英語講演種別：基調講演開催地：Fukuoka

In this talk, we talk the state of the art of pervasive sensing

technology and its applications, including understanding nursing

processes in hospitals, and helping households to recognize daily

activities related to energy consumption.

Recent deployment of light-weight sensors equipped with wireless

networking or embedded in smart phones made it possible to recognize

and understand the activities and contexts of the users. If we can

compile them in an understandable manner to the users, we can expect

various applications such as in healthcare and energy management

domains.

In this new horizon of technology, we introduce the state of the art

of the research, including utilization and improvement of machine

learning methods, such as being robust for labels with uncertain

segments, or utilizing periodical information for daily activities.

Moreover, we also introduce the activity of collecting and providing a

large-scale dataset for activity recognition, such as a mobile

accelerator open dataset with about 35,000 activity data from more

than 200 subjects, nurses' sensor data combined with 100 patients'

sensor data and medical records, and 34 households' light sensor data

set for 4 months combined with smart meter data.

(Invited) Pervasive Sensing for Nursing and Smart Energy Applications

International Conference on Informatics, Electronics & Vision (ICIEV) 2015年06月 http://sozolab.jp/publications/308

(Invited) Pervasive Sensing for Nursing and Smart Energy Applications

International Conference on Informatics, Electronics & Vision (ICIEV) 2015年06月

開催期間： 2015年06月16日発表言語：英語講演種別：特別講演開催地：Kitakyushu, Japan

In this talk, we talk the state of the art of pervasive sensing technology and its applications, including understanding nursing processes in hospitals, and helping households to recognize daily activities related to energy consumption.

Recent deployment of light-weight sensors equipped with wireless networking or embedded in smart phones made it possible to recognize and understand the activities and contexts of the users. If we can compile them in an understandable manner to the users, we can expect various applications such as in healthcare and energy management domains.

In this new horizon of technology, we introduce the state of the art of the research, including utilization and improvement of machine learning methods, such as being robust for labels with uncertain segments, or utilizing periodical information for daily activities.

Moreover, we also introduce the activity of collecting and providing a large-scale dataset for activity recognition, such as a mobile accelerator open dataset with about 35,000 activity data from more than 200 subjects, nurses' sensor data combined with 100 patients' sensor data and medical records, and 34 households' light sensor data set for 4 months combined with smart meter data.

センサビッグデータを用いた看護行動／節電行動理解

パーティクルフィルタ研究会発足十周年記念イベント 2015年04月 http://sozolab.jp/publications/306

Mobile Activity Recognition and Healthcare Sensing

KIT/AIST Brain Circulation workshop 2015年03月 http://sozolab.jp/publications/307

HASCシーケンスデータの品質の検証

HASC Challenge報告会 2015年02月 http://sozolab.jp/publications/296

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