Intelligent Interaction for Human-Friendly Service Robot in Smart House Environment

Abstract

The smart house under consideration is a service-integrated complex system to assist older persons and/or people with disabilities. The primary goal of the system is to achieve independent living by various robotic devices and systems. Such a system is treated as a human-in-the loop system in which humanrobot interaction takes place intensely and frequently. Based on our experiences of having designed and implemented a smart house environment, called Intelligent Sweet Home (ISH), we present a framework of realizing human-friendly HRI (human-robot interaction) module with various effective techniques of computational intelligence. More specifically, we partition the robotic tasks of HRI module into three groups in consideration of the level of specificity, fuzziness or uncertainty of the context of the system, and present effective interaction method for each case. We first show a task planning algorithm and its architecture to deal with well-structured tasks autonomously by a simplified set of commands of the user instead of inconvenient manual operations. To provide with capability of interacting in a human-friendly way in a fuzzy context, it is proposed that the robot should make use of human bio-signals as input of the HRI module as shown in a hand gesture recognition system, called a soft remote control system. Finally we discuss a probabilistic fuzzy rule-based lifelong learning system, equipped with intention reading capability by learning human behavioral patterns, which is introduced as a solution in uncertain and time-varying situations.

Management of Uncertainty and Spatio-Temporal Aspects for Monitoring and Diagnosis in a Smart Home

“…A Smart Home 32,33,34,35,36,37,38 can be described as a house that is supplemented with technology, for example sensors and devices, in order to increase the range of services provided to its occupants by reacting in an intelligent way. The technology will have two main components: a set of sensors and a networking layer linking those sensors with computing facilities.…”

EMG Methods for Evaluating Muscle and Nerve Function

“…The focus in this application is the magnitude of the signal. It has also been proposed that EMG and EEG can be used as a form of human/robot interaction where a robot/device would sense EMG/EEG signals to learn about nearby humans, not explicitly for control (Bien, et al, 2008). EMG techniques can also be used to monitor users for fatigue, alertness, or other variables in various situations.…”

Editorial: Inaugural issue

“…Three leading countries in the area are Japan, Singapore and Korea. Some examples of research and development programs in those countries are: WHT (the Welfare Techno Houses) [10] related to Chiba University (Japan), the Aware Home project [17] of NII (Japan), the Human-friendly Welfare Robot System Research Center [4] at KAIST (Korea) and the Institute for Infocomm Research (Singapore) [12]. Companies like Philips, Siemens, Nokia, Microsoft, SAP, VTT, and several European Telecoms are investing heavily in the areas of Ambient Intelligence and Smart Environments.…”