Tracking and detection of pointing gesture in 3D space
In this paper, we discuss pointing gesture estimation in the context of Human Robot Interaction (HRI). Pointing gesture provides and utilizes information in Kukanchi (Interactive Human Space Design and Intelligence) more natural than other conservative method like keyboard or mouse. Therefore, we design a user interface adopting pointing gesture to manipulate objects and robot naturally in 3D space. Initially, human posture is tracked. Later, the information is used to detect the dynamic pointing gesture using our proposed method which is Hidden Markov Model (HMM). Preliminary experiment shows the average angular error of pointing direction is less than seven degrees.
In hospitals, offices and other settings, professionals face the challenge of accessing and sharing sensitive content in public areas. As tablets become increasingly adopted in work environments, it is important to explore ways to support privacy that are appropriate for tablet use in dynamic, mobile workflows. In this research we consider how spatial information can be utilized to support both individual and collaborative work in a natural way while respecting data privacy. We present a proof-of-concept implementation of a proximity-aware tablet, and a range of privacy notification and control mechanisms designed for such a tablet. Results from a user study support the idea that interpersonal distance and orientation can be used to mediate privacy management for tablet interfaces. Selecting a specific design for privacy threat notification and response is highly context-dependent-for example, in health care the first priority is to not impede the fluid exchange of information.