Human-centered safety infrastructure
Integrating physiological, behavioral and subjective measures during XR use to quantify sickness, cognitive load and walking risk, and building state-estimation models and safety evaluation protocols.
RESEARCHER×ENGINEERcreate the principlescale it to society
As a Ph.D. student, I research how to measure the state of people using XR so they can collaborate safely across the real and the virtual. Through Nexreal LLC, I also take on software development for companies, including large-scale SaaS.
My goal is the “Interverse”, where the real and the virtual merge so people can work together beyond the limits of place, body and time.
For XR to become everyday infrastructure, we need to measure human limits such as sickness, cognitive load and walking risk, and design collaborative spaces that span the real and the virtual around them. These are my two themes.
Integrating physiological, behavioral and subjective measures during XR use to quantify sickness, cognitive load and walking risk, and building state-estimation models and safety evaluation protocols.
Clarifying design principles for co-presence and task performance in asymmetric collaboration that mixes CAVE, HMD and AR.
AR sickness has so far been measured only by questionnaires after the experience, missing how it develops and the signs before people notice it. We record gaze, ECG and EEG together during AR use to find which physiological responses track the strength of sickness. From there, we aim to build objective indicators that catch sickness before symptoms appear.
Looking at information on AR glasses while walking can draw attention away and slow reactions to the surroundings. We measure reaction time, gait and autonomic responses together when content is shown at the center or the edge of the view, and quantify how placement changes the load. From the results, we aim to derive design guidelines for AR displays that stay safe while walking.
How good a 360° image looks when projected in a CAVE differs from person to person and does not match objective quality metrics. We test whether the three types of quality preference we found are specific to the immersive CAVE or stable traits of individuals, by comparing the same images in the CAVE and on a regular screen. From there, we aim to build image quality guidelines suited to immersive environments.
Characters stamped on parts have little contrast with their background and look different under different lighting, so images alone cannot read them reliably. We test how far recognition improves with preprocessing that combines image texture (2D) and surface shape from a 3D sensor (3D). We aim for reliable automatic reading of stamped characters for industrial inspection.
Through Nexreal LLC I take on whichever stages a project needs, from requirements to operations, and carry products beyond PoC all the way to production.
Adopted by municipalities
As part of a development team working with PMs and QA, built AI features that support students’ learning and teachers’ instruction in a domestic education SaaS used by millions.
TypeScript · Next.js · AWS · Amazon Aurora · GraphQL · Python · FastAPI
Workload cut by over half
Automated candidate touchpoints in recruiting, from calls and SMS to scheduling and assigning staff.
Express · React · Supabase · Google Workspace API · GCP · Docker
Real-time
Consolidated scattered sales data into one real-time view of revenue, conversions, win rate and LTV, for both management decisions and individual performance.
TypeScript · Express · Google Apps Script · Supabase · Retool · GCP · Docker
Used by several clients
A bot that answers questions in Slack based on internal documents.
TypeScript · Slack Bolt · Dify · Supabase · GCP · Docker
生理指標の経時変化特性を用いたAR酔い感受性の予測
2026 IEEJ Electronics, Information and Systems Society Conference
A Hybrid Preprocessing Approach for Stamped Character Recognition using 2D Texture and 3D Shape Features
2026 International Symposium on Flexible Automation (ISFA 2026)
生理指標を用いたAR酔いの客観的評価に関する検討
2026 Annual Meeting of the IEEJ
没入型立体環境における360度画像の投影 ―第2報―
IPSJ 88th National Convention
Mirroring Agent:自己の感情状態を反映するエージェントの設計とその対話がもたらす自己認識への影響
The 30th VRSJ Annual Conference
3D-OCR AIとDX
JSST 9th NBC Technical Committee Meeting
没入型立体環境における360度画像の投影
IPSJ 87th National Convention
音声認識を用いた疑似会話による英語学習コンテンツの開発
2024 Annual Meeting of the IEEJ
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Nexreal LLC
Software Engineer — software and R&D
NEXASPARK Inc.
Engineering support for client projects
RCAST, The University of Tokyo
System development for research projects
SICK K.K.
Applied research and development for sensors
Information Technology Center, Tokai University
Researcher
Digital Design Studio Inc.
Eye-tracking support for a Tokyo-funded XR project
JST SPRING
Fellow
Graduate School of Science and Technology, Tokai University
Ph.D. program (expected March 2029)
Graduate School of Information and Telecommunication Engineering, Tokai University
Master’s program
Department of Information Media Technology, Tokai University
Bachelor’s program
For research collaboration, development work, talks or interviews, please reach me at the address below.