Towards the development of an EIT-based stretchable sensor for multi-touch industrial human-computer interaction systems

In human-computer interaction studies, an interaction is often considered as a kind of information or discrete internal states of an individual that can be transmitted in a loss-free manner from people to computing interfaces (or robotic interfaces) and vice-versa. This project aims to investigate processes capable of communicating and cooperating by adjusting their schedules to match the evolving execution circumstances, in a way that maximise the quality of their joint activities. By enabling human-computer interactions, the process will emerge as a framework based on the concept of expectancy, demand, and need of the human and computer together, for understanding the interplay between people and computers. The idea of this work is to utilise touch feedback from humans as a channel for communication thanks to an artificial sensitive skin made of a thin, flexible, and stretchable material acting as transducer. As a proof of concept, we demonstrate that the first prototype of our artificial sensitive skin can detect surface contacts and show their locations with an image reconstructing the internal electrical conductivity of the sensor

Social Stories in Robot Assisted Therapy for Children with ASD

Mobile technology devices are commonly used as assistive technology to support children with Autism Spectrum Disorder (ASD) in gaining skills in interpersonal communication. While considered gener- ally safe and e ective, there are concerns that a child that is taught to communicate through interactive technologies may become dependent on the virtual world and its rewards, while interpersonal skills are sacri ced or not generalized to real world settings (Bauminger-Zviely, Eden, Zancanaro, Weiss, & Gal, 2013). This chapter theorizes that the anthropomorphic embodiment of humanoid robots may provide a compromise between the real and the virtual worlds. The authors suggest that a humanoid robot can use social stories within an Applied Behavioral Analysis (ABA) framework to support the acquisition of social interaction skills of children with ASD. The objective of this chapter is to contribute to the current literature by providing a description of this intervention and make suggestions for its implementation using a case study approach

How can social robots spark collaboration and engagement among people with intellectual disability?

Social robots have been successfully used in previous research to develop social behaviours among participants with Autism Spectrum Disorder (ASD). Technology has often been found to be a contributing factor to heightened engagement in learning activities, including for people with intellectual disability. This research proposes to build on these two opportunities by exploring the potential of social robots to elicit social interaction, cooperation and engagement among groups of adults with intellectual disability. The study presented here involved observation, semi structured interviews and video analysis of six participants with intellectual disability interacting with a social robot in a series of five weekly workshops. The robot used for this study was Cozmo</p

The Therapeutic Use of Humanoid Robots for Behavioral Disorders

In this work, we illustrate an innovative treatment for patients affected by Behavioral Disorders, that relies on the use of Pepper humanoid robot. This new therapeutic methodology was created to support and make the therapist's work more attractive. Pepper is equipped with a tablet and two identical cameras. The tablet is used to let the patient interact with the application, while the cameras are used to capture their real-time emotions to understand the degree of attention and any difficulty that they may have. The interaction with the tablet takes place through some exercises in the form of games. The exercises performed by the subject are analyzed and combined with the data captured by the cameras. The combination of these data is processed to propose appropriate levels of therapeutic activities. This process leads to the digitization of the patient's healing path so that any improvement (or worsening) is monitored and causes Pepper to become a reliable and predictable technological intermediary for the child. The work has been developed in collaboration with a diagnostic and therapeutic center. Interacting with a humanoid robot, children exhibit a higher engagement, which can be explained, according to the psychologists, by the fact that a robot is emotionally less rich than human beings, and the patient feels less scared

Working with a social robot in school: A long-term real-world unsupervised deployment

Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study

An Ensemble Classifier Based on Three-Way Decisions for Social Touch Gesture Recognition

Social touch is an important form of social interaction. In Human Robot Interaction (HRI), touch can provide additional information to other modalities, such as audio, visual. One of the application is the robot therapy that has great social significance. In this paper, an ensemble classifier based on threeway decisions is proposed to recognize touch gestures. Firstly, features are extracted from on six perspectives and four classifiers are constructed on different scales with different pre-processing methods. . Then an ensemble classifier is used to combine the four classifiers to classify the gestures. The proposed method is tested on the public Corpus of Social Touch (Cost) dataset. The experiments results not only verify the validity of our method but also show the better accuracy of our ensemble classifier