Integration of Technology Within Intervention Strategies for Students With High Functioning Autism: A Phenomenological Approach to Analyzing Educators’ Viewpoints

There is a phenomenon that exists within the Maryland State Public School System regarding technology integration within intervention strategies for students with high functioning autism (HFA). Educators have attested that there is minimally available technology for consistent use when working with their students during intervention strategies and services. Thus, when stakeholders understand the actual experiences of the professionals that work with students that have HFA on a daily basis, positive reform may occur at the immediate level by administrators within school buildings. The purpose of this study was to examine how general and special educators experienced technology use during interventions that they provided to their students with HFA. There were two main research questions: How do general and special educators describe their experiences using technology during interventions for students with HFA? What factors are IEP team committee members considering when they decide to include or refrain from adding technology accommodations within an IEP for students with HFA? The instrumentation utilized in this study was a set of open-ended questions conducted in an interview format. After careful analysis of the data collected, six main themes were detected connected to the conceptual framework of educational equity, persuasive technology, and theory of mind. The necessity of serious funding reform for technology within this particular county are the implications for future practices in the Maryland State public school system. Provision of technology including electronic devices, adequate professional development, and increased funding will equalize educational access for disabled students with HFA

Socially Assistive Robot Enabled Home-Based Care for Supporting People with Autism

The growing number of people diagnosed with Autism Spectrum Disorder (ASD) is an issue of concern in Australia and many countries. In order to improve the engagement, reciprocity, productivity and usefulness of people with ASD in a home-based environment, in this paper the authors report on a 9 month Australian home-based care trial of socially assistive robot (Lucy) to support two young adults with autism. This work demonstrates that by marrying personhood (of people with ASD) with human-like communication modalities of Lucy potentially positive outcomes can be achieved in terms of engagement, productivity and usefulness as well as reciprocity of the people with ASD. Lucy also provide respite to their carers (e.g., parents) in their day to day living

A Reflection on Virtual Reality Design for Psychological, Cognitive & Behavioral Interventions: Design Needs, Opportunities & Challenges

Despite the substantial research interest in using Virtual Reality (VR) in healthcare in general and in Psychological, Cognitive, and Behavioral (PC&B) interventions in specific, as well as emerging research supporting the efficacy of VR in healthcare, the design process of translating therapies into VR to meet the needs of critical stakeholders such as users and clinicians is rarely addressed. In this paper, we aim to shed light onto the design needs, opportunities and challenges in designing efficient and effective PC&B-VR interventions. Through analyzing the co-design processes of four user-centered PC&B-VR interventions, we examined how therapies were adapted into VR to meet stakeholders’ requirements, explored design elements for meaningful experiences, and investigated how the understanding of healthcare contexts contribute to the VR intervention design. This paper presents the HCI research community with design opportunities and challenges as well as future directions for PC&B-VR intervention design

RC2S: A Cognitive Remediation Program to Improve Social Cognition in Schizophrenia and Related Disorders

In people with psychiatric disorders, particularly those suffering from schizophrenia and related illnesses, pronounced difficulties in social interactions are a key manifestation. These difficulties can be partly explained by impairments in social cognition, defined as the ability to understand oneself and others in the social world, which includes abilities such as emotion recognition, theory of mind (ToM), attributional style, and social perception and knowledge. The impact of several kinds of interventions on social cognition has been studied recently. The best outcomes in the area of social cognition in schizophrenia are those obtained by way of cognitive remediation programs. New strategies and programs in this line are currently being developed, such as RC2S (cognitive remediation of social cognition) in Lyon, France. Considering that the social cognitive deficits experienced by patients with schizophrenia are very diverse, and that the main objective of social cognitive remediation programs is to improve patients’ functioning in their daily social life, RC2S was developed as an individualized and flexible program that allows patients to practice social interaction in a realistic environment through the use of virtual reality techniques. In the RC2S program, the patient’s goal is to assist a character named Tom in various social situations. The underlying idea for the patient is to acquire cognitive strategies for analyzing social context and emotional information in order to understand other characters’ mental states and to help Tom manage his social interactions. In this paper, we begin by presenting some data regarding the social cognitive impairments found in schizophrenia and related disorders, and we describe how these deficits are targeted by social cognitive remediation. Then we present the RC2S program and discuss the advantages of computer-based simulation to improve social cognition and social functioning in people with psychiatric disorders

Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Recommendations for Developing Video Games to Address Depression Among College Students

Depression is a significant problem on college campuses, and the data shows that prevalence is on the rise (The National Survey on Drug Use and Health [NSDUH], 2014; Stewart, Ricci, Chee, Hahn, & Morgenstein, 2003). Depression impacts the well-being of students and puts them at risk for a variety of issues (Leach, 2009; Adams, Wharton, Quilter, & Hirsch, 2008; Serras, Saules, Cranford, & Eisenberg, 2010; Cranford, Eisenberg, & Serras, 2009; Weitzman, 2004). Many students with depression do not receive care (Blanco, Okuda, Wright, Hasin, Grant, Liu, & Olfson, 20008; Eisenburg and Chung, 2012), or receive care that is not minimally adequate (Eisenburg and Chung, 2012). The first aim of this paper is to review current literature from three pertinent areas of research: (a) depression in the university student population; (b) community psychology; and (c) video games as psychological interventions. These reviews are then synthesized to provide the basis for recommendations for the development of a prevention program that engages the university community in the creation of a video game based intervention for students at risk for, or suffering from, depression. These recommendations are based on principles outlined by Nation et al., (2003). By using existing theory based in community psychology, a video game based prevention program to supplement existing university mental health services is not only feasible but may significantly improve the treatment of depression on college campuses

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD

Development and evaluation of a haptic framework supporting telerehabilitation robotics and group interaction

Telerehabilitation robotics has grown remarkably in the past few years. It can provide intensive training to people with special needs remotely while facilitating therapists to observe the whole process. Telerehabilitation robotics is a promising solution supporting routine care which can help to transform face-to-face and one-on-one treatment sessions that require not only intensive human resource but are also restricted to some specialised care centres to treatments that are technology-based (less human involvement) and easy to access remotely from anywhere. However, there are some limitations such as network latency, jitter, and delay of the internet that can affect negatively user experience and quality of the treatment session. Moreover, the lack of social interaction since all treatments are performed over the internet can reduce motivation of the patients. As a result, these limitations are making it very difficult to deliver an efficient recovery plan. This thesis developed and evaluated a new framework designed to facilitate telerehabilitation robotics. The framework integrates multiple cutting-edge technologies to generate playful activities that involve group interaction with binaural audio, visual, and haptic feedback with robot interaction in a variety of environments. The research questions asked were: 1) Can activity mediated by technology motivate and influence the behaviour of users, so that they engage in the activity and sustain a good level of motivation? 2) Will working as a group enhance users’ motivation and interaction? 3) Can we transfer real life activity involving group interaction to virtual domain and deliver it reliably via the internet? There were three goals in this work: first was to compare people’s behaviours and motivations while doing the task in a group and on their own; second was to determine whether group interaction in virtual and reala environments was different from each other in terms of performance, engagement and strategy to complete the task; finally was to test out the effectiveness of the framework based on the benchmarks generated from socially assistive robotics literature. Three studies have been conducted to achieve the first goal, two with healthy participants and one with seven autistic children. The first study observed how people react in a challenging group task while the other two studies compared group and individual interactions. The results obtained from these studies showed that the group interactions were more enjoyable than individual interactions and most likely had more positive effects in terms of user behaviours. This suggests that the group interaction approach has the potential to motivate individuals to make more movements and be more active and could be applied in the future for more serious therapy. Another study has been conducted to measure group interaction’s performance in virtual and real environments and pointed out which aspect influences users’ strategy for dealing with the task. The results from this study helped to form a better understanding to predict a user’s behaviour in a collaborative task. A simulation has been run to compare the results generated from the predictor and the real data. It has shown that, with an appropriate training method, the predictor can perform very well. This thesis has demonstrated the feasibility of group interaction via the internet using robotic technology which could be beneficial for people who require social interaction (e.g. stroke patients and autistic children) in their treatments without regular visits to the clinical centres