Most children can naturally engage in play and by this, develop skills while interacting with their peers and toys. However, children with Autism Spectrum Disorder (ASD) often show impairments in play skills which result in limited opportunities for interaction with others and the learning of social skills. In this regard, robotic devices/toys that can provide simple and attractive indications are advantageous to engage children with ASD in play activities that require social and interaction skills. This project proposes a new interaction method using paired robotic devices called COLOLO to facilitate a fundamental exchange of intention in communication so-called turn-taking. These tangible devices are designed to sense the user’s manipulation, send a message to the paired device, and display visual cues for assisting children to achieve turn-taking through play. On the sessions with COLOLO there are two devices, one held by the therapist and one by the child, and they take turns to manipulate the toys and change their colors. In this article, two experimental conditions or interaction rules: the “two-sided lighting rule” and the “one-sided lighting rule" were introduced. The two interactions rules differ from each on the way the devices used the visual cues to indicate the turn-holder. The effect of each interaction rule on children’s turn-taking behaviors was investigated through an experimental study with four children with ASD. From the results, we found that with the one-sided lighting rule participants tended to shift their gaze more and to decrease the failed attempts of turn-taking. The discussion covers the possibilities of using paired devices to describe participants’ behaviors related to turn-taking quantitatively.

In socially assistive robotics, especially for children with autism spectrum disorder (ASD), adapting the behavior of the robot according to the personal characteristics of each individual is one of the important challenges. Machine learning techniques are promising approaches to endow a robot with the capability of adapting its behavior through the interaction. It is critical to prepare a rich data set such as a set of behaviors with teaching signals for each individual with ASD to allow application of the state-of-the-art machine learning techniques; however, this is typically difficult to prepare in advance owing to the diverseness of ASD and the complexity of the motion design of the robot. This paper proposes a framework to acquire the personalized behavior set of a robot by combining a robot teleoperation method and a wearable device for detecting the affective cue of a child with ASD while interacting with the robot. The developed system allows the human operator to improvise the robot's behavior flexibly in real-time to explore the preferred interaction manner and motion patterns of each child. The preferred motion patterns are extracted and evaluated based on the affective state of the child estimated by the wearable device, and stored in the personal database for each individual with ASD. We conducted a free-interaction experiment with ten participants with ASD and demonstrated that the proposed system successfully described the interaction between the robot and the participant for acquiring the appropriate behaviors of the robot.

This paper presents the design, implementation, and application of a vision-based automatic system that measures facial interaction based on human's cognitive feature. We investigated the feature of people's facial interaction under natural gaze-movement via an experiment, and created criteria of k-degree facial interaction and face-to-face interaction depending only on facial orientation. These criteria were used to develop the automatic system. The system is vision-based. It could be easily embedded into applications. We focused on an application to understand the behavior of children with autism spectrum disorders (ASD), and tested the use of the automatic system in a robot-assisted activity for those children. The results suggested that the system could help to improve the efficiency of behavior analysis during the children's activities with the robot. The facial interaction measured between the children and the robot can be used by therapists to comprehend the children's psychological aspects and state of health.

We introduce Pepita, a caricatured huggable robot capable of sensing and conveying affective expressions by means of tangible gesture recognition and projected avatars. This study covers the design criteria, implementation and performance evaluation of the different characteristics of the form and function of this robot. The evaluation involves: (1) the exploratory study of the different features of the device, (2) design and performance evaluation of sensors for affective interaction employing touch, and (3) design and implementation of affective feedback using projected avatars. Results showed that the hug detection worked well for the intended application and the affective expressions made with projected avatars were appropriated for this robot. The questionnaires analyzing users’ perception provide us with insights to guide the future designs of similar interfaces.

This study outlines the development of a platform that enables real-time bio-information sharing between gaming partners regardless of the game of choice. The authors hypothesize that since biosignals contain information about a person’s physiological and psychological state, sharing bio-information with easy-to-understand visual representations can augment the sense of social presence between online gaming partners. The proposed method uses the heart rate as a biosignal detected using a wrist-worn heart rate monitor, uploads these signals to a secure online real-time database, and creates visual representations of the gaming partner’s heart rate on each gamer’s screen. Comparative studies were conducted with 20 gamers. The results showed that sharing bio-information could elicit a sense of social presence similar to sharing real-time face videos in online settings. Furthermore, a synergetic effect was observed when bio-information was combined with face video sharing, leading to an even greater perceived social presence. Similar trends were not observed in co-presence in the participants of this study. However, grouping the participants’ scores by emotional arousal level showed a similar trend in the high-arousal group, suggesting that the impact of bio-information sharing may be modulated by the intensity of the gaming experience. The obtained results underscore the potential of bio-information sharing to augment social presence in online gaming contexts.

The accident probability of beginner drivers is significantly higher than that of experienced drivers. It can be assumed that this is due to lack of driving skills which lead to making wrong decisions according to cognition and operating in correct way. In this paper, we propose a novel assisted driving system intended to help drivers to improve their skills for the reverse parking. The system is able to assist the driver by haptic instruction on the steering wheel in order to induce the driver to make the adequate operation. For the validation, we developed a 1/10 scale car simulator as a simulation environment on which we installed the proposed assistance method and conducted reverse parking experiment by using the simulator. According to the experiment, we validated that the parking accuracy and the trajectory similarity of subjects assisted by proposed system significantly increased compare to subjects unassisted. Consequently, the proposed assisted driving system could accelerate the learning of humans' driving skills.