Our research uses the data which can be acquired from the real world. The data we focus on are (but not limited to):
- Human activity data (inertia data, chat log, etc.)
- People flow data (GPS log, Wi-Fi log, passing log, etc.)
- Operation data of public transportation
We have 3 research groups.
Urban Computing Group conducts researches on wider area, for more efficient and comfortable life. Analysis targets are traffic-related data (bus operation records), people-flow-related data (GPS, Wi-Fi sensing, camera) and so on. This group also develops a distributed simulation platform with a multi-city-level area, massive amount of mixed domain agents, and dynamic extensibility.
Affective Computing Group is focusing on realizing a society in which computers, robots, and humans coexist and cooperate by analyzing human emotions and sensibilities.Currently, we are conducting research that can be applied to a variety of fields, such as dialogue systems with emotional response, and optimization of pickers and delivery robots.
Human-Centric Computing Group is focusing on technologies and resources that fit human capabilities. We are currently exploring indoor positioning with inertial sensors, dance performance recognition with multi-modal sensors, and user's behavior analysis with location information.
THis project is related to 3 teams above. This project aims a new way of logistics and people flow in the world where autonomous vehicles run everywehere. Topics are environment recognition using autonomous vehicle, large scale (city level) people flow simulation, etc.
HASC (Human Activity Sensing Consortium) is working to collect and to distribute large-scale human activity corpus.
Context estimation team researches on the recognition of human activity and the estimation of the surrounding environment from various data. We use the data which can be collected by inertial sensors of smartphones, signal strength of Bluetooth devices, magnetic strength etc.
For example, human activity recognition aims accurate recognition of our daily activity such as walking, sitting down, climbing the stairs. Another example is location estimation indoors. We cannot use GPS in the building and underground areas. To make our devices provide the suitable information based on the situation which the users are facing.
3D information processing team researches on "artificial intelligence which can understand the environment." We use RGB image, thermal image, and other sensor data to realize automatic re-creation of space map and recognition of scene context and so on. We are doing 3D modeling, change detection, scene recognition etc. using core methods such as image processing, deep learning.
Super-Smart society team researches on data analysis on transportation data, human flow, etc. to realize highly efficient society. Research topics are: delay estimation using bus operation data, socoiety-wide infrastructure system with demand-supply concept.