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Real-World Intelligence Foundation Lab
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The lab studies AI that recognizes 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 studies artificial intelligence that estimates the behavior, state, and context of humans and animals from multiple types of sensor data.
Spanning IoT, wearables, biologging, activity recognition, localization, edge AI, and Physical AI, the lab designs sensors, data, algorithms, and implementation as one integrated whole.
The lab aims to establish an intelligence foundation that works in real-world environments.
On the lab's website, it is also introduced under the name “Maekawa Lab.”
Key terms
- Activity recognition
- AI technology that recognizes the behavior of an observed subject from data captured by sensors and similar devices
- Biologging
- A method of studying the ecology and behavior of animals using devices attached to them
- Indoor positioning
- Technology that estimates the positions of people, robots, and other objects indoors, where GPS does not reach
NewsLab news
A doctoral student received the International Presentation Encouragement Award at the IPSJ SIG on Ubiquitous Computing Systems (UBI)
A doctoral student received the Outstanding Paper Award at the IPSJ SIG on Ubiquitous Computing Systems (UBI)
A master's student received the UBI Young Researcher Award
A student was selected for 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 prize
ResearchResearch themes
The Real-World Intelligence Foundation Lab studies artificial intelligence that estimates the behavior and state of humans and animals from sensor data. Its main areas are human activity recognition, AI biologging, indoor positioning, and real-world data mining.
*Illustrative imageAI that recognizes human behavior
The lab studies AI technology that recognizes human behavior from sensor data. It researches and develops real-world AI that recognizes and understands complex behaviors, such as those in smart factories and life science experiments, and applies it to coaching and industrial robots.
*Illustrative imageAI that observes the ecology of wild animals
The lab was the first in the world to propose a method in which AI mounted on a biologging device intelligently observes the ecology of wild animals. Through work such as developing small, smart AI that runs on tiny devices, the lab takes on the mysteries of wildlife.
*Illustrative imageEstimating the positions of people and robots indoors
The lab studies technology that estimates the position coordinates of people and robots using acceleration data and Wi-Fi signals obtained from sensors such as those in smartphones. It also works on multimodal positioning that additionally uses audio and GPS signals observed indoors.
*Illustrative imageUncovering new knowledge from behavioral data
The lab develops data mining technology that uncovers previously unknown knowledge by realizing AI that automatically analyzes the behavioral data of humans and animals. Expected applications include drug discovery through behavioral analysis of model animals and extraction of the expertise of skilled workers.
MembersFaculty
The members of the Real-World Intelligence Foundation Lab consist of faculty, collaborating researchers, technical assistants, administrative assistants, and doctoral, master's, and undergraduate students.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: “Smartwatch-828786” Crew / CC0 (cropped)
- AI that recognizes human behavior: “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 that observes the ecology of wild animals: “African wild dog with a GPS Tracking collar in Laikipia” Kengitau254 / CC BY-SA 4.0 (cropped)
- Estimating the positions of people and robots indoors: “Wi-Fi Cantenna” lungstruck from Northeast Ohio, USA / CC BY-SA 2.0 (cropped)
- Uncovering new knowledge from behavioral data: “Ethology diversity” DrChrissy / CC BY-SA 3.0 (cropped)
- Related lab (Intelligent Information Systems Lab): “Wikimedia Foundation Servers-8055 03” Victorgrigas / CC BY-SA 3.0 (cropped)
- Related lab (Data Generation Engineering Lab): “দোল উৎসব” Latentlife.archive / CC BY-SA 4.0 (cropped)
- Related lab (Advanced High-Performance Computing Infrastructure Systems Lab): “CSCS Supercomputer Alps 2” CS / CC BY-SA 3.0 (cropped)
- Related lab (Multimedia Agent Lab): “EZ-Robot Humanoid software” Bobjgalindo / CC BY 4.0 (cropped)
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