Miroslava Migovich, Deeksha Adiani, Michael Breen, A. Swanson, Timothy J. Vogus, Nilanjan Sarkar
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Stress Detection of Autistic Adults during Simulated Job Interviews using a Novel Physiological Dataset and Machine Learning
The interview process has been identified as one of the major barriers to employment of autistic individuals, which contributes to the staggering rate of under and unemployment of autistic adults. Decreasing stress during the interview has been shown to improve interview performance. However, in order to effectively provide insights on stress to both interviewees and interviewers, it is necessary to first effectively measure stress. This work explores physiological stress detection through wearable sensing as a means of obtaining quantitative stress measures from young autistic adults undergoing a virtual simulated interview using supervised machine learning techniques. Several supervised learning models were explored and it was found that Elastic Net Regression had the best accuracy with individual models with an accuracy of 84.8% while Support Vector Regression models evaluated with leave-one-out cross validation had a group accuracy of 75.4%. The predictions from the stress model were used with data visualization techniques in order to provide insights on the interview process from both a group and individual viewpoint, showing that stress trends can be found and evaluated using the stress model. This work also addresses a major gap in physiological stress detection literature by presenting a novel dataset of physiological data and ground truth labels for 15 autistic young adults undergoing a simulated interview.
期刊介绍:
Computer and information technologies have re-designed the way modern society operates. Their widespread use poses both opportunities and challenges for people who experience various disabilities including age-related disabilities. That is, while there are new avenues to assist individuals with disabilities and provide tools and resources to alleviate the traditional barriers encountered by these individuals, in many cases the technology itself presents barriers to use. ACM Transactions on Accessible Computing (TACCESS) is a quarterly peer-reviewed journal that publishes refereed articles addressing issues of computing that seek to address barriers to access, either creating new solutions or providing for the more inclusive design of technology to provide access for individuals with diverse abilities. The journal provides a technical forum for disseminating innovative research that covers either applications of computing and information technologies to provide assistive systems or inclusive technologies for individuals with disabilities. Some examples are web accessibility for those with visual impairments and blindness as well as web search explorations for those with limited cognitive abilities, technologies to address stroke rehabilitation or dementia care, language support systems deaf signers or those with limited language abilities, and input systems for individuals with limited ability to control traditional mouse and keyboard systems. The journal is of particular interest to SIGACCESS members and delegates to its affiliated conference (i.e., ASSETS) as well as other international accessibility conferences. It serves as a forum for discussions and information exchange between researchers, clinicians, and educators; including rehabilitation personnel who administer assistive technologies; and policy makers concerned with equitable access to information technologies.