Finger Mouse and Text-To-Speech Application as Additions to the Smart Wheelchair

The smart wheelchair project is a unique investigation into the possibilities of helping the impaired navigate in a mobile chair. Many disabled people who need the help of a wheelchair to move about also need help communicating orally. This project allows the “walking” wheelchair to do some “talking.” A communication program for the wheelchair was developed, as well as a finger mouse that was implemented into all the programs. The finger mouse is a switch button small enough to wear on one’s finger. When the button is pressed, a signal is sent out from the transmitter and picked up by the receiver, which sends a signal through the parallel port of the computer to execute the desired application. The communication program is a speech program that speaks text messages. The finger mouse and speech application are connected through a communication display interface — a page of icons. When the mouse is clicked over an icon linked to a particular phrase, the mouse, the display, and the speech software work together to speak the phrase. Thus, the communication program gives the freedom of speech to anyone using the wheelchair for free range of motion

A Systematic Approach To Teaching Critical Thinking Skills To Electrical And Computer Engineering Undergraduates

Coursework that instills patterns of rigorous logical thought has long been a hallmark of the engineering curriculum.  However, today’s engineering students are expected to exhibit a wider range of thinking capabilities both to satisfy ABET requirements and to prepare the students to become successful practitioners.  This paper presents the initial results from a systematic effort to incorporate broader critical thinking instruction and assessment into electrical and computer engineering education as part of a university-wide quality enhancement program.  All incoming freshmen are given explicit and implicit instruction in critical thinking in ENGR 100: Introduction to Engineering and other engineering fundamentals courses, using the Paul-Elder framework of critical thinking to define and operationalize critical thinking.  This critical thinking foundation is reinforced later in the disciplinary courses so that students integrate critical thinking with the basic principles and practices of engineering.  In the Electrical and Computer Engineering (ECE) program, at the sophomore level, students use critical thinking skills which were developed during their engineering fundamentals courses, to analyze requirements and constraints which would apply in real-world design projects.  At the junior level, similar use of critical thinking is applied in an introductory computing methods course; and at the senior level, critical thinking skills are again strengthened and assessed in the capstone design course and in the professional issues and current topics seminar.  The latter course emphasizes understanding of professional ethics and current topics in electrical and computer engineering.  Initial data from this pilot implementation indicates statistically significant improvement in critical thinking skills in ECE students who have progressed through this sequence, and as a side benefit, it appears that writing skills also improve

Social Impressions of the NAO Robot and its Impact on Physiology

The social applications of robots possess intrinsic challenges with respect to social paradigms and heterogeneity of different groups. These challenges can be in the form of social acceptability, anthropomorphism, likeability, past experiences with robots etc. In this paper, we have considered a group of neurotypical adults to describe how different voices and motion types of the NAO robot can have effect on the perceived safety, anthropomorphism, likeability, animacy, and perceived intelligence of the robot. In addition, prior robot experience has also been taken into consideration to perform this analysis using a one-way Analysis of Variance (ANOVA). Further, we also demonstrate that these different modalities instigate different physiological responses in the person. This classification has been done using two different deep learning approaches, 1) Convolutional Neural Network (CNN), and 2) Gramian Angular Fields on the Blood Volume Pulse (BVP) data recorded. Both of these approaches achieve better than chance accuracy 25% for a 4 class classification.Comment: Accepted for the Special Track on Affective Robotics (AFFRO) of ACII 202

Physiological Signal Analysis for Emotion Estimation of Children with Autism Spectrum Disorder

The diagnosis of Autism Spectrum Disorder (ASD) in children is based on human observations by a clinician. The medical evaluation assesses deficits in social communication, social interaction, and restricted, repetitive behaviors. Robotic technology can assist in quantitatively measuring the observations to be used as a future tool for autism diagnosis and intervention. The project explores this technology to produce robotic partners that can adapt to the needs of the ASD population. This way, such robots could serve as instructors or learning peers. A friendly, partner robot, specifically designed for children with ASD could be used to investigate the effect of therapy and the connection between the motor, sensory, and emotional cortex in the brains of children with ASD. Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human emotions. The robots are envisioned to interpret physiological signals and appropriately adapt to the emotional responses of a user. Research has found that physiological pattern recognition can potentially aid in assessing and quantifying emotions. Thus, one of the purposes in this research is to analyze physiological data collected from human subjects to show its relationship to changes in emotional reactions during different activities. This poster illustrates observations made of the Heart Rate signal collected during different activities performed in a human-subject study by six patients with a diagnosis of ASD. The Heart Rate signal analysis shows a statistical difference that can support broader research in this area of interest

Affect Recognition in Robot Assisted Rehabilitation of Children with Autism Spectrum Disorder *

Abstract –This paper presents a novel affect-sensitive human-robot interaction framework for rehabilitation of children with autism spectrum disorder (ASD). The overall aim is to enable the robot to detect and respond to the affective cues of the children in order to help them explore social interaction dynamics in a gradual and adaptive manner. The first part of the proposed framework, namely the ‘affect recognition’ module is developed in detail in this paper. Two tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism rehabilitation. Affective cues are inferred from psychophysiological analysis that uses subjective reports of the affective states from a therapist, a parent, and the child himself/herself. Comprehensive physiological indices are investigated that may correlate with the affective states of children with ASD. A support vector machines based affect recognizer is designed that yielded reliable prediction with approximately 83 % success when using the therapist’s reports. This is the first time, to our knowledge, such a human-robot interaction framework for autism rehabilitation is proposed. This is also the first time that the affective states of children with ASD have been experimentally detected via physiologybased affect recognition technique