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Real-World Intelligence Foundation Lab
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The lab researches AI that understands the behavior of people and animals from sensor data.
AboutAbout the lab
Creating AI that understands the real world.
The Real-World Intelligence Foundation Lab researches artificial intelligence that estimates the behavior of humans and animals from sensor data.
Spanning fields such as IoT, wearables, and biologging, the lab designs sensors, data, algorithms, and implementation as one integrated whole, aiming to establish an intelligence foundation that works in real environments.
The research proceeds in four areas: human activity recognition, AI biologging, indoor positioning, and real-world data mining.
The lab's website also introduces it under the name "Maekawa Lab."
Key terms
- Biologging
- A method of studying the ecology and behavior of animals from data collected by sensors attached to wild animals
- Activity recognition
- AI technology that recognizes the behavior of an observed subject from data observed by sensors or similar devices
- Indoor positioning
- Technology that estimates the positions of people and objects indoors, where GPS does not reach
NewsLab news
A student received the International Presentation Encouragement Award at the Special Interest Group on Ubiquitous Computing Systems (UBI) of the Information Processing Society of Japan
A student received the Outstanding Paper Award at the Special Interest Group on Ubiquitous Computing Systems (UBI) of the Information Processing Society of Japan
A student received the UBI Young Researcher Award at the Special Interest Group on Ubiquitous Computing Systems (UBI) of the Information Processing Society of Japan
A student received the Osaka University Outstanding Research Award for Female Graduate Students (FY2025)
A master's student received the Graduate School of Information Science and Technology Award, the department's top award
ResearchResearch themes
The Real-World Intelligence Foundation Lab's goal is to "create artificial intelligence that understands the real world." Targeting humans and animals, the lab researches artificial intelligence that estimates behavior, state, and context from the data of multiple sensors.
Human Activity Recognition
This is AI technology that recognizes human behavior from sensor data. The lab conducts research and development that understands complex behavior in smart factories and life science experiments and applies it to coaching and industrial robots, and also builds large-scale datasets and benchmarks.
*Illustrative imageAI Biologging
This is a method, first proposed in the world by the lab, in which AI mounted on a biologging device intelligently observes the ecology of wild animals. Through work such as developing small, smart AI, the lab takes on mysteries of wildlife that have remained unexplained until now.
*Illustrative imageIndoor Positioning
The lab estimates the positions of people and robots using acceleration data and Wi-Fi signals obtained from sensors in devices such as smartphones. It researches multimodal indoor localization that also incorporates audio and GPS signals observed indoors.
*Illustrative imageReal-World Data Mining
The lab develops technology that automatically analyzes behavior data of humans and animals to uncover knowledge that has remained unknown until now. Expected applications include using behavior analysis of model animals in drug discovery and extracting the know-how of skilled workers.
MembersFaculty
The members of the Real-World Intelligence Foundation Lab consist of faculty, project researchers, joint researchers, doctoral, master's, and undergraduate students, technical assistants, and administrative assistants.As of 2026
- ProfessorTakuya Maekawa
- LecturerQingxin Xia
- Project Assistant ProfessorRyoma Otsuka
- Project Assistant ProfessorMomoe Sukegawa
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Schematic diagram of a remote health monitoring system based on wearable sensors” Aaron Asael Smith, Rui Li, Zion Tsz Ho Tse / CC BY 4.0 (cropped)
- AI Biologging: “Seabirds LC0141” Jörg Hempel / CC BY-SA 2.0 de (cropped)
- Indoor Positioning: “Indoor location services on ( )” Intel Free Press / CC BY-SA 2.0 (cropped)
- Real-World Data Mining: “Ethology diversity” DrChrissy / CC BY-SA 3.0 (cropped)
- Related lab (Organic Flexible Systems Lab): “LG Electronics organic electro-luminescence in EDION NAMBA” Tokumeigakarinoaoshima / CC BY-SA 4.0 (cropped)
- Related lab (Integrated Information Design Lab): “CW-01 National Semiconductor Scientific Calculator Watch CMOS-LSI circuit dies” Mister rf / CC BY-SA 4.0 (cropped)
- Related lab (Human Information Engineering Lab): “User wearing affective haptic devices” Dzmitry Tsetserukou / CC BY-SA 3.0 (cropped)
- Related lab (Information Systems Architecture Lab [Graduate School of Information Science and Technology]): “Embedded World 2014 RIoT-Board Freescale” Ordercrazy / CC0 (cropped)
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