Human Behavior Recognition for Daily Task Assistance using Sparse Range Data Observations

In this paper, we describe our methods of detecting human behavior in order to monitor and assist daily human tasks. To assist people in performing activities in daily life, we must be able to understand the system user´s situation/state in the current task: what information is useful for the user now? Our posture-detection system using IR cameras and invisible IR pattern projectors detects the user´s state as his/her 3D appearance (posture). In the proposed system, human behavior is modeled as a distribution of 3D appearances using kernel density functions, and the results of this behavior detection are used to determine the instructions to be given to the user. We show a sample implementation of the system for a toilet task with voice- and CG-animation-based instructions to users. By using this method, we can achieve effective task assistance and reveal limited visual representation of users, taking into account the human need for personal privacy. Finally, we show experimental results of using the proposed method for human tracking and behavior monitoring