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Shimosaka Lab
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A lab that interprets human behavior from everyday sensor data.
AboutAbout the lab
Human behavior, as sensors see it.
The Shimosaka Lab aims to realize innovative intelligent information processing in the fields of mobile, wearable, and ubiquitous computing.
Based on sensing data collected naturally in daily life, the lab understands and models human behavior and the intentions behind it.
By combining large-scale data such as location data and search histories with wearable sensors and wireless signals, the lab aims to understand and predict human mobility and individual behavior more deeply.
The lab has also worked on returning its technology to industry, including the real-world deployment of human mobility prediction systems.
Key terms
- Ubiquitous computing
- Information processing in which devices and sensors in our surroundings blend into daily life and work without our being aware of them.
- Inverse reinforcement learning
- A machine learning method that estimates the goals and evaluation criteria behind a behavior from data of exemplary behavior.
- POI
- Information about points that have meaning on a map, such as shops and facilities. Short for Point of Interest.
NewsLab news
A master's student receives the Outstanding Paper Award from the IPSJ Special Interest Group on Ubiquitous Computing Systems (UBI)
A master's student receives the UBI Young Researcher Award from the UBI Special Interest Group
A student receives the International Conference Presentation Encouragement Award from the UBI Special Interest Group
A student receives the Student Encouragement Award from the UBI Special Interest Group
A student receives the Outstanding Paper Award from the UBI Special Interest Group
ResearchResearch themes
The lab studies how to make machines understand human behavior and its intent, based on data collected from smartphone location data, wearable sensors, and wireless signals. It also places importance on expanding into application areas such as energy management, urban and transportation systems, healthcare, and recommender systems.
*Illustrative imageDestination Prediction Using Nearby Facility Information
This research predicts where a person will go next from past movement trajectories. Taking 500 m mesh cells that could be destinations as candidates, the lab combines the information on facilities (points of interest, or POIs) around each cell with the trajectory history to evaluate how likely each cell is to be the destination.
*Illustrative imageReading Hand and Finger Operations with a Wrist Sensor
By combining an ultrasonic sensor and an IMU worn on the wrist, the lab captures both the state of fingertip contact and the movement of the arm at the same time. This makes it possible to distinguish in detail even operations whose wrist trajectories are similar, such as scrolling and typing.
*Illustrative imageLearning Safe Driving Behavior
The lab works on inverse reinforcement learning, which learns human driving from data. Focusing on the fact that dangerous behavior appears only during a small part of a trajectory, the lab treats only those segments as negative examples, which improved the convergence and stability of learning.
*Illustrative imageMeasuring Breathing with Radio Waves at the Wrist
Breathing waveforms are a clue to health status and cognitive load, but devices such as chest belts are burdensome to wear. The lab estimated breathing waveforms from the reflection of UWB radio waves with a device worn on the wrist, and confirmed how posture and movement affect accuracy.
MembersFaculty
The members of the Shimosaka Lab consist of faculty, support staff, research collaborators, and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorMasamichi ShimosakaPublications ↗
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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: “Warsaw city center in winter at night December 2023” Oleslawlama / CC BY-SA 4.0 (cropped)
- Destination Prediction Using Nearby Facility Information: “Olkhon Island, GPS navigation, Smartphone, Lake Baikal, Russia” Vyacheslav Argenberg / CC BY 4.0 (cropped)
- Reading Hand and Finger Operations with a Wrist Sensor: “AGENT smartwatch prototype by Secret Labs - WP )” Pete Brown from Gambrills, MD, USA / CC BY 2.0 (cropped)
- Learning Safe Driving Behavior: “Car dashboard on highway (Unsplash)” Arkady Lifshits overdriv3 / CC0 (cropped)
- Measuring Breathing with Radio Waves at the Wrist: “Apple Watch back sensors” Mliu92 / CC BY-SA 3.0 (cropped)
- Related lab (高安美佐子研究室): “Market centre in Tokyo stock exchange” ehnmark / CC BY 2.0 (cropped)
- Related lab (瀧ノ上正浩研究室): “2T7A4” Yma3591 / CC BY 4.0 (cropped)
- Related lab (小野
（峻） 研究室): “Cable racks at grid computing center, Fermilab with blue lights” ENERGY.GOV / Public domain (cropped) - Related lab (吉瀬研究室): “Cray 1 IMG 9126” Rama / CC BY-SA 2.0 fr (cropped)
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