Research Profile

Yuuki Nishiyama, Ph.D.

Assistant Professor, Center for Spatial Information Science, The University of Tokyo

Yuuki Nishiyama is an Assistant Professor at the Center for Spatial Information Science in the University of Tokyo. He obtained M.S.(2014) in Media and Governance from Keio University, and Ph.D. in Media and Governance (2017) from Keio University, respectively. He had worked at Keio University in Japan and the University of Oulu in Finland, as a post-doctoral researcher respectively. He started work at the Institute of Industrial Science in the University of Tokyo as a Research Associate in 2019, and has held his current position since 2022. His current research interests include ubiquitous computing systems, mobile-wearable sensing platforms, and human ability augmentation. He is a member of ACM, IEEE, and Information Processing Society of Japan (IPSJ). 

Background

  • Assistant Professor : Center for Spatial Information Science, The University of Tokyo (Apr. 2022 – current)
  • Research Associate : Institute of Industrial Science, The University of Tokyo (Jul. 2019 – Mar. 2022)
  • Postdoctoral Researcher: Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland (Mar. 2018 – Jun. 2019) 
  • Postdoctoral Researcher: Graduate School of Media and Governance, Keio University, Japan (Aug. 2017 – Mar. 2018)
  • Research Assistant: Global Environmental System Leader (GESL) program, Graduate School of Media and Governance, Keio University, Japan (Apr. 2014 – Mar. 2017)

News

  • [2024.10.13] I had an invited talk about “Estimating the state of a humen mental and physical condition using sensors on smartphone and machine learning,” at a special session of Humans Living in a Digital Society.
  • [2024.10.8] We received the Best Poster Award at ACM UbiComp 2024 for our research on Investigating Acceptable Voice-based Notification Timings through Earable Devices: A Preliminary Field Study.”
  • [2024.10.8] We received the Best Poster Award at ACM UbiComp 2024 for our research on Toward Detecting Student-Athletes’ Condition Using Passive Mobile and Wearable Sensing.
  • [2024.07.16] A full paper, “Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data,” was accepted at ACM CIKM2024.
  • [2024.07.01] Two poster papers are accepted by ACM UbiComp2024@Melbourne, Australia.
  • [2024.05.16] I had an invited talk at Mobile AI Systems which is a workshop in ACM MobiSys2024. The talk title is “In-the-wild IoT: Lessons from real-world implementations.”
  • [2024.04.24] Two poster papers are accepeted by ACM MobiSys2024@Tokyo.
  • A full paper, “RideGuard: Micro-Mobility Steering Maneuver Prediction with Smartphones,” was accepted at IEEE ICDCS2024@New Jersey.  
  • A full paper, “ReHEarSSE: Recognizing Hidden-in-the-Ear Silently Spelled Expressions,” was accepted at ACM CHI2024@Hawaii.  
  • I received an Outstanding Reviewer Award at the 25th ACM International Conference on Multimodal Interaction (ICMI2023) in Paris, France.  
  • I presented Smartwatch-Based Sensing Framework for Continuous Data Collection: Design and Implementation at a workshop (Mental Health and Well-being: Sensing & Intervention) in ACM UbiComp23@Cancun.
  • A Late Breaking Results paper “Assessing Infant and Toddler Behaviors through Wearable Inertial Sensors: A Preliminary Investigation” was accepted in ACM ICMI2023.
  • A full paper “HeadSense: Visual Search Monitoring and Distracted Behavior Detection for Bicycle Riders” was accepted in IEEE WoWMoM2023.
  • A full paper “HeadMon: Head Dynamics Enabled Riding Maneuver Prediction” was accepted in IEEE PerCom2023.
  • We had two presentations about AthelteLife and UV estimation using GNSS Signal Strength in IPSJ SIG UBI78.

Research Topics

  • Context-aware technologies and systems: 
    • Technology for recognizing and predicting the behavior of micro-mobility riders to improve the safety of micro-mobility
    • Detection technology of infant/toddler behavior and parenting behavior related behaviors to support child-rearing
    • Methods of detecting infection prevention behavior (i.e., hand-washing, mask-usage, and conversation events) using consumer devices
  • Passive mobile and wearable sensing and its platforms:
    • Postpartum depression detection using passive sensing technology
    • Development of information platform for improving the performance and condition of athletes using mobile/wearable devices: Athlete Life, MiQ, and Ripken.
    • Open-source passive mobile/wearable sensing platform (mainly iOS, watchOS, and Android): AWARE, Senbay, and MOCHA
  • Human behavior change and intervention methods:
    • Platform for promoting team-level behavior change using competitive and cooperative lifelog sharing
    • Investigation of the impact of the way incentives are offered on infectious disease prevention behavior

Selected Publication

Selected Awards