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...siastically applied to human-computer interaction and other domains such as driving, flying, machine operation [6][7]. However, the application of this technique in robotics domain is relatively less =-=[8]-=-. Several past and ongoing research works emphasize on human machine interaction based on affect recognition from physiological signals. Picard and colleagues have employed a combination of Sequential...

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... approaches infinity. The sum based danger criterion is comprised of the inertia factor and the center of mass distance factor described above, as follows: DC sum�W i•f Isum �I s��W d•f CMsum �D CM�. =-=(4)-=- Here, W i and W d are weights of the inertia and distance term, scaled such that W i�W d�1. The weights W i and W d are tuned based on the particular robot structure. For low inertia robots, and when...

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...lity of this approach, an experiment was conducted testing the human subjective and physiological response to various robot motions. The physiological response was then classified using fuzzy methods =-=[5, 7]-=- to determine if physiological response can be used as a mode of communication during human – robot interaction. Early results of this work indicate that physiological signals have potential as an add...

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...nitor indirect communication signals from humans and use them to infer intent. One bene£t of monitoring intent is that such information can allow robots to anticipate changes in behaviours in humans (=-=[9]-=-) and therefore can respond in a more timely and safe manner. Although focusing on physiological signals to betray human intent, such as emotional state and arousal level, [9] discuss intent as a cont...

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...ctivity. These three signals have been shown to be the most reliable indicators of affective state in psychophysiological research [10, 24, 25]. Respiration rate was also considered in an early study =-=[11]-=-, but was rejected as unsuitable for on-line interaction applications due to the slow physiological response of the signal. Skin conductance (SC) is a strong indicator of affective arousal. Several st...

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...articipants involvedsin the problem solving sessions. They found that the decision tree learning classification system was more reliablesthan the fuzzy logic system of classification.sKulić and Croft =-=[16]-=- began their series of research studiessby estimating participant intent using physiological signalssand performed some preliminary tests. They felt that bysdetermining participants’ intent through ph...

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...aceted nature of interaction. More specifically, researchers should consider collecting: (a) data concerning the human user's immediate physiological or other responses to a robot's behaviour [10][11]=-=[13]-=-[15], (b) data concerning the description of the activity being pursued [12][16][17], (c) the human user's opinions and attitudes concerning the interactions observed and/or under investigation Manusc...

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...ally applied to human-computer interaction [15] and other domains such as driving [16], flying [17], and machine operation [18], the application of this technique in robotics domain is relatively new =-=[19]-=-. Our preliminary work in [20] presented concepts and initial experiment for a natural and intuitive human-robot interaction framework based on detection of human affective states from physiological s...

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...own that emotions and physiology (biological signals such as heart activity, muscle tension, blood pressure, skin conductance etc.) are closely intertwined and one influences the other. Research in affective computing, pioneered by Picard exploits this relationship between emotions and physiology to detect human affective states [14]. While concepts from psychophysiology are now being enthusiastically applied to human-computer interaction [15] and other domains such as driving [16], flying [17], and machine operation [18], the application of this technique in robotics domain is relatively new [19]. Our preliminary work in [20] presented concepts and initial experiment for a natural and intuitive human-robot interaction framework based on detection of human affective states from physiological signals by a robot. The paper is organized as follows: Section II presents the scope and rationale of the paper. Section III describes our proposed theoretical framework for detecting affective cues in human-robot interaction. This section provides the details of the physiological indices used for anxiety detection, use of regression tree for prediction of anxiety and the control A New Approach to ...