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Action and Behavior Analysis Lab
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The lab uses video analysis to turn people's behavior into numerical data for observation.
AboutAbout the lab
Reading human behavior in numbers from video.
The lab led by Assistant Professor Eigo Nishimura studies methods for observing, in numerical form, how people behave when they use tools and devices, using video analysis technology.
Behavioral observation is a design research method for finding what to improve in tools and devices, but it requires observer experience and a great deal of effort.
The lab aims to reduce the effort of observation and make its results more objective through video analysis.
The lab develops applications that support behavioral observation, and also uses them in joint development with companies and in R&D consulting.
Key terms
- Behavioral observation
- A method of watching how people behave when using tools and devices in order to identify points for improvement
- Keypoint detection
- A technology that finds specific points on a person's body (keypoints) in video and tracks their movement
- Pose estimation
- A technology that estimates the positions of a person's body parts from video or images
NewsLab news
A paper on Timeline-kun, an app for managing the progress of experiments, was published in the Journal of Open Research Software
A floor wiper by KOKUYO, developed with the lab's collaboration on ergonomics, won a Good Design Award
A paper on a behavior analysis method using pose estimation was published in Behavior Research Methods
ResearchResearch themes
The lab studies methods for examining, in numerical form, how users behave when they use tools and devices, using video analysis technology. Its aim is to make behavioral observation less labor-intensive and more objective.
*Illustrative imageQuantitative Behavioral Observation Through Video Analysis
The lab uses video analysis technology to turn user behavior into numerical data, aiming to make behavioral observation less labor-intensive and more objective. The goal is to reduce the dependence on observer experience and the large amount of effort that observation requires.
Developing an Application to Support Behavioral Observation
The lab develops Behavior Senpai, an application that uses a keypoint detection AI to convert the behavior of people in video into time-series coordinate data, so that the speed and direction of movements can be analyzed.
Exploratory Analysis of Behavior Using Pose Estimation
The lab studies a method that converts video into time-series data of interpretable features and efficiently analyzes a wide variety of posture patterns. It is intended for the exploratory stage, before a coding scheme has been decided.
MembersFaculty
- Assistant ProfessorEigo Nishimura
VoicesStudent voices
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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: “Estudio de Motion Capture en Funcom” Jesusbella / CC BY-SA 4.0 (cropped)
- Quantitative Behavioral Observation Through Video Analysis: “Bones Studio hala” Gabriela Cybulska / CC BY-SA 4.0 (cropped)
- Related lab (Action and Behavior Analysis Lab): “VSDC Motion Tracking” Fever Dream / CC BY-SA 4.0 (cropped)
- Related lab (Kansei and Behavioral Science Lab): “Sunrise at Bodie Island Light Station” CapeHatterasNPS / Public domain (cropped)
- Related lab (Kansei and Behavior Science Lab): “Effet de la pollution lumineuse sur l'arbre urbain Effect of light pollution on urban trees and dead leaves 04” Lamiot / CC BY-SA 4.0 (cropped)
- Related lab (Green Space Conservation Lab): “Oak woodland after the CCC’s cut down encroaching conifers ( )” USFWS Pacific Southwest Region / Public domain (cropped)
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