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Body language is an important aspect of human communication, which an effective human-robot interaction interface should mimic well. Human beings exchange information and convey their thoughts and feelings through gaze, facial expressions, body language and tone of voice along with spoken words, and infer 65% of the meaning of the communicated messages from these nonverbal cues. Modern robotic platforms are however limited in their ability to automatically generate behaviours that align with their speech. In this paper, we develop a neural network based system that takes audio from a user as an input and generates upper-body gestures including head, hand and torso movements of the user on a humanoid robot, namely, Softbank Robotics' Pepper. Our system was evaluated quantitatively as well as qualitatively using web-surveys when driven by natural speech and synthetic speech. We compare the impact of generic and personspecific neural network models on the quality of synthesised movements. We further investigate the relationships between quantitative and qualitative evaluations and examine how the speaker's personality traits affect the synthesised movements.

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Robotic telepresence aims to create a physical presence for a remotely located human (teleoperator) by reproducing their verbal and nonverbal behaviours (e.g. speech, gestures, facial expressions) on a robotic platform. In this work, we propose a novel teleoperation system that combines the replication of facial expressions of emotions (neutral, disgust, happiness, and surprise) and head movements on the fly on the humanoid robot Nao. Robots' expression of emotions is constrained by their physical and behavioural capabilities. As the Nao robot has a static face, we use the LEDs located around its eyes to reproduce the teleoperator expressions of emotions. Using a web camera, we computationally detect the facial action units and measure the head pose of the operator. The emotion to be replicated is inferred from the detected action units by a neural network. Simultaneously, the measured head motion is smoothed and bounded to the robot's physical limits by applying a constrained-state Kalman filter. In order to evaluate the proposed system, we conducted a user study by asking 28 participants to use the replication system by displaying facial expressions and head movements while being recorded by a web camera. Subsequently, 18 external observers viewed the recorded clips via an online survey and assessed the quality of the robot's replication of the participants' behaviours. Our results show that the proposed teleoperation system can successfully communicate emotions and head movements, resulting in a high agreement among the external observers (ICCE = 0.91, ICCHP = 0.72).

In this paper we focus on detection of deception and suspicion from electrodermal activity (EDA) measured on left and right wrists during a dyadic game interaction. We aim to answer three research questions: (i) Is it possible to reliably distinguish deception from truth based on EDA measurements during a dyadic game interaction? (ii) Is it possible to reliably distinguish the state of suspicion from trust based on EDA measurements during a card game? (iii) What is the relative importance of EDA measured on left and right wrists? To answer our research questions we conducted a study in which 20 participants were playing the game Cheat in pairs with one EDA sensor placed on each of their wrists. Our experimental results show that EDA measures from left and right wrists provide more information for suspicion detection than for deception detection and that the person-dependent detection is more reliable than the person-independent detection. In particular, classifying the EDA signal with Support Vector Machine (SVM) yields accuracies of 52% and 57% for person-independent prediction of deception and suspicion respectively, and 63% and 76% for person-dependent prediction of deception and suspicion respectively. Also, we found that: (i) the optimal interval of informative EDA signal for deception detection is about 1 s while it is around 3.5 s for suspicion detection; (ii) the EDA signal relevant for deception/suspicion detection can be captured after around 3.0 seconds after a stimulus occurrence regardless of the stimulus type (deception/truthfulness/suspicion/trust); and that (iii) features extracted from EDA from both wrists are important for classification of both deception and suspicion. To the best of our knowledge, this is the first work that uses EDA data to automatically detect both deception and suspicion in a dyadic game interaction setting.