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The paper reports our research aiming at utilization of human interactive communication modeling principles in application to a novel interaction paradigm designed for brain-computer/machine-interfacing (BCI/BMI) technologies as well as for socially aware intelligent environments or communication support systems. Automatic procedures for human affective responses or emotional states estimation are still a hot topic of contemporary research. We propose to utilize human brain and bodily physiological responses for affective/emotional as well as communicative interactivity estimation, which potentially could be used in the future for human-machine/environment interaction design. As a test platform for such an intelligent human-machine communication application, an emotional stimuli paradigm was chosen to evaluate brain responses to various affective stimuli in an emotional empathy mode. Videos with moving faces expressing various emotional displays as well as speech stimuli with similarly emotionally articulated sentences are presented to the subjects in order to further analyze different affective responses. From information processing point of view, several challenges with multimodal signal conditioning and stimuli dynamic response extraction in time frequency domain are addressed. Emotions play an important role in human daily life and human-to-human communication. This is why involvement of affective stimuli principles to human-machine communication or machine-mediated communication with utilization of multichannel neurophysiological and periphery physiological signals monitoring techniques, allowing real-time subjective brain responses evaluation, is discussed. We present our preliminary results and discuss potential applications of brain/body affective responses estimation for future interactive/smart environments. [ABSTRACT FROM AUTHOR]

When a human user interacts with an adaptive agent to achieve human-agent collaboration, active adaptation is considered to be one of the critical characteristics of the agent. In order to investigate the principal features of active adaptation, we developed a human-agent collaborative experimental environment called WAITER (waiter agent interactive training experimental restaurant) and conducted two types of experiments, a Wizard of OZ (WOZ) agent experiment and an autonomous agent experiment. The objective of these experiments is to observe how human users perceive the agents and change their instructions when interacting with adaptive agents. The results indicate that humans can recognize changes in the agent's actions and change their instruction methods accordingly. This implies that active adaptation of the agents may encourage the adaptation of the human users and help to build an adaptation loop between them. The experimental results also suggest that active adaptation may play an important role in a human-agent collaborative task. [ABSTRACT FROM AUTHOR]

For a robot to cohabit with people, it should be able to learn people's nonverbal social behavior from experience. In this paper, we propose a novel machine learning method for recognizing gestures used in interaction and communication. Our method enables robots to learn gestures incrementally during human-robot interaction in an unsupervised manner. It allows the user to leave the number and types of gestures undefined prior to the learning. The proposed method (HB-SOINN) is based on a self-organizing incremental neural network and the hidden Markov model. We have added an interactive learning mechanism to HB-SOINN to prevent a single cluster from running into a failure as a result of polysemy of being assigned more than one meaning. For example, a sentence: ''Keep on going left slowly'' has three meanings such as, ''Keep on (1)'', ''going left (2)'', ''slowly (3)''. We experimentally tested the clustering performance of the proposed method against data obtained from measuring gestures using a motion capture device. The results show that the classification performance of HB-SOINN exceeds that of conventional clustering approaches. In addition, we have found that the interactive learning function improves the learning performance of HB-SOINN. [ABSTRACT FROM AUTHOR]

The goal of motif discovery algorithms is to efficiently find unknown recurring patterns. In this paper, we focus on motif discovery in time series. Most available algorithms cannot utilize domain knowledge in any way which results in quadratic or at least super-linear time and space complexity. In this paper we define the Constrained Motif Discovery problem which enables utilization of domain knowledge into the motif discovery process. The paper then provides two algorithms called MCFull and MCInc for efficiently solving the constrained motif discovery problem. We also show that most unconstrained motif discovery problems be converted into constrained ones using a change-point detection algorithm. A novel change-point detection algorithm called the Robust Singular Spectrum Transform (RSST) is then introduced and compared to traditional Singular Spectrum Transform using synthetic and real-world data sets. The results show that RSST achieves higher specificity and is more adequate for finding constraints to convert unconstrained motif discovery problems to constrained ones that can be solved using MC-Full and MCInc. We then compare the combination of RSST and MCFull or MCInc with two state-of-the-art motif discovery algorithms on a large set of synthetic time series. The results show that the proposed algorithms provided four to ten folds increase in speed compared the unconstrained motif discovery algorithms studied without any loss of accuracy. RSST+MCFull is then used in a real world human-robot interaction experiment to enable the robot to learn free hand gestures, actions, and their associations by watching humans and other robots interacting. [ABSTRACT FROM AUTHOR]

Future robots are expected to be operated by untrained persons using natural means of communication. This entails the necessity of understanding how humans will communicate with such robots especially in the non-humanoid case where anthropomorphism is in its minimum. This paper presents a controlled experiment to study the interaction between untrained human users and a miniature robot in a collaborative navigation task. Three dimensions of the interaction are studied. The first dimensions is the gesture use by the operators during the interaction. A consistent usage pattern with slight cultural effect was discovered in the data. The second dimension is human adaptation to both the task requirements and robot capabilities. Three main findings about the human adaptation are reported. The third studied dimension is the effectiveness and naturalness of using motion cues as a feedback mechanism from the robot in comparison with verbal feedback. The results of the experiment showed that there is no significant difference in the task completion accuracy and time or in the feeling of naturalness between motion cues and verbal feedback, and there is a statistically significant improvement when using either of them compared with the control case. Moreover, the subjects selected the motion cues feedback mechanism more frequently as the preferred feedback modality for them. These findings support the use of nonverbal motion feedback for knowledge transfer and information acquisition from non-humanoid robots in collaborative navigation tasks. [ABSTRACT FROM AUTHOR]