Akane Sano
生駒 : 奈良先端科学技術大学院大学, 2022.10
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Digital phenotyping and machine learning technologies have shown a potential to measure objective behavioral and physiological markers, provide risk assessment for people who might have a high risk of poor health and wellbeing, and help make better decisions or behavioral changes to support health and wellbeing. I will introduce a series of studies, algorithms, and systems we have developed for measuring, predicting, and supporting personalized health and wellbeing. I will also discuss challenges, learned lessons, and potential future directions in health and wellbeing research.
2022
電子化映像資料(1時間25分23秒）
情報科学領域・コロキアム ; 2022年度
講演者所属: Rice University
講演日: 2022年10月18日 2限
講演場所: 情報科学棟 エーアイ大講義室(L1)
Japan
English (eng)
English (eng)
