Visual and Hearing Sensitivity Affect Robot-Based Training for Children Diagnosed With Autism Spectrum Disorder

In this paper, we investigate the impact of sensory sensitivity during robot-assisted training for children diagnosed with Autism Spectrum Disorder (ASD). Indeed, user-adaptation for robot-based therapies could help users to focus on the training, and thus improve the benefits of the interactions. Children diagnosed with ASD often suffer from sensory sensitivity, and can show hyper or hypo-reactivity to sensory events, such as reacting strongly or not at all to sounds, movements, or touch. Considering it during robot therapies may improve the overall interaction. In the present study, thirty-four children diagnosed with ASD underwent a joint attention training with the robot Cozmo. The eight session training was embedded in the standard therapy. The children were screened for their sensory sensitivity with the Sensory Profile Checklist Revised. Their social skills were screened before and after the training with the Early Social Communication Scale. We recorded their performance and the amount of feedback they were receiving from the therapist through animations of happy and sad emotions played on the robot. Our results showed that visual and hearing sensitivity influenced the improvements of the skill to initiate joint attention. Also, the therapists of individuals with a high sensitivity to hearing chose to play fewer animations of the robot during the training phase of the robot activity. The animations did not include sounds, but the robot was producing motor noise. These results are supporting the idea that sensory sensitivity of children diagnosed with ASD should be screened prior to engaging the children in robot-assisted therapy.

Visual and hearing sensitivity affect robot-based training for children diagnosed with autism spectrum disorder

In this paper, we investigate the impact of sensory sensitivity during robot-assisted training for children diagnosed with Autism Spectrum Disorder (ASD). Indeed, user-adaptation for robot-based therapies could help users to focus on the training, and thus improve the benefits of the interactions. Children diagnosed with ASD often suffer from sensory sensitivity, and can show hyper or hypo-reactivity to sensory events, such as reacting strongly or not at all to sounds, movements, or touch. Considering it during robot therapies may improve the overall interaction. In the present study, thirty-four children diagnosed with ASD underwent a joint attention training with the robot Cozmo. The eight session training was embedded in the standard therapy. The children were screened for their sensory sensitivity with the Sensory Profile Checklist Revised. Their social skills were screened before and after the training with the Early Social Communication Scale. We recorded their performance and the amount of feedback they were receiving from the therapist through animations of happy and sad emotions played on the robot. Our results showed that visual and hearing sensitivity influenced the improvements of the skill to initiate joint attention. Also, the therapists of individuals with a high sensitivity to hearing chose to play fewer animations of the robot during the training phase of the robot activity. The animations did not include sounds, but the robot was producing motor noise. These results are supporting the idea that sensory sensitivity of children diagnosed with ASD should be screened prior to engaging the children in robot-assisted therapy.

Quantification of Demand-Supply Balancing Capacity among Prosumers and Consumers: Community Self-Sufficiency Assessment for Energy Trading

With the increased adoption of distributed energy resources (DERs) and renewables, such as solar panels at the building level, consumers turn into prosumers with generation capability to supply their on-site demand. The temporal complementarity between supply and demand at the building level provides opportunities for energy exchange between prosumers and consumers towards community-level self-sufficiency. Investigating different aspects of community-level energy exchange in cyber and physical layers has received attention in recent years with the increase in renewables adoption. In this study, we have presented an in-depth investigation into the impact of energy exchange through the quantification of temporal energy deficit–surplus complementarity and its associated self-sufficiency capacities by considering the impact of variations in community infrastructure configurations, variations in household energy use patterns, and the potential for user adaptation for load flexibility. To this end, we have adopted a data-driven simulation using real-world data from a case-study neighborhood consisting of ~250 residential buildings in Austin, TX with a mix of prosumers and consumers and detailed data on decentralized DERs. By accounting for the uncertainties in energy consumption patterns across households, different levels of PV and energy storage integration, and different modalities of user adaptation, various scenarios of operations were simulated. The analysis showed that with PV integration of more than 75%, energy exchange could result in self-sufficiency for the entire community during peak generation hours from 11 a.m. to 3 p.m. However, there are limited opportunities for energy exchange during later times with PV-standalone systems. As a potential solution, leveraging building-level storage or user adaptation for load shedding/shifting during the 2-h low-generation timeframe (i.e., 5–7 p.m.) was shown to increase community self-sufficiency during generation hours by 17% and 5–10%, respectively, to 83% and 71–76%.

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive “tool” whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.

End-user satisfaction of technology-enabled assessment in higher education: A coping theory perspective

The current study examines students’ coping process of a forced technological intervention in academic outcome assessment in a higher education setting. A mixed-method approach was used to study 246 post-graduate students’ post-usage behaviour of electronic tablet-PC exams and examined their end-user satisfaction. This is an empirical study grounded in the Coping Model of User Adaptation (CMUA). Respondents of the study comprise of post-graduate students, who were exposed to an innovative digital device for writing descriptive exams as a substitute to the conventional paper-mode exam. Data were analyzed using SPSS and Nvivo. Findings indicate that problem-focused coping has a significant influence on end-user satisfaction, and on the contrary emotion-focused coping is insignificant among the students. The study offers insights into those institutions, which are aspiring to advance with similar interventions in academic outcome assessment. The study contributes to the literature on technostress, coping strategy, and end-user satisfaction of ICT.