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Arvind Satyanarayan. Haghighi, Nava. Massachusetts Institute of Technology. Engineering and Management Program. System Design and Management Program. Massachusetts Institute of Technology. Integrated Design and Management Program. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Engineering and Management Program Massachusetts Institute of Technology. Integrated Design and Management Program Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2021-10-08T16:48:39Z 2021-10-08T16:48:39Z 2020 2020 https://hdl.handle.net/1721.1/132823 1262990999 Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020 Cataloged from the official version of thesis. Includes bibliographical references (pages 105-114). In this thesis, I introduce Self-Interfaces as a method for creating behavior change. Self-Interfaces are interfaces that intuitively communicate relevant aspects of covert physiological signals through biofeedback to give the user insight into their behavior and assist them in creating behavior change. The human heartbeat is a good example of an intuitive and relevant haptic biofeedback; it does not distract and is only felt when the heart beats fast. My vision is to identify other covert physiological processes and instances in which they become useful, and augment our awareness of those signals in order to create behavior change. As a first case-study, I develop the Self-Interface for Electrodermal Activity (EDA), which is designed to help regulate attention and interest in users with Attention Deficit Hyperactivity Disorder (ADHD). EDA is a covert physiological signal correlated with high and low arousal affective states. Three studies were carried out to: 1. identify the design criteria for development of the EDA Self-Interface, 2. identify guidelines to reduce the cognitive load imposed by the haptic biofeedback signal, and 3. identify the aspects of the EDA that are relevant and insightful for the ADHD population. The insights from these studies contributed to the design and development of the EDA Self-Interface which has three components: EDA Sensor (Affectiva E4 Sensor), a wearable haptic biofeedback interface, and a phone app to process the EDA data and communicate it with the wearable interface. Lastly, I discuss the evaluation criteria for the EDA Self-Interface and propose a longitudinal study for such evaluation. by Nava Haghighi. S.M. in Engineering and Management S.M. S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 114 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Engineering and Management Program. System Design and Management Program. Integrated Design and Management Program. Electrical Engineering and Computer Science. Self-Interfaces : utilizing real-time biofeedback in the wild to elicit subconscious behavior change Utilizing real-time biofeedback in the wild to elicit subconscious behavior change Thesis application/pdf 2021-10-08T16:48:39Z Publication Master SysDes EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="fe27a6e6-fda9-4049-b0f0-367df6f5cb1a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Self-Interfaces : utilizing real-time biofeedback in the wild to elicit subconscious behavior change</Title> <Subtitle>Utilizing real-time biofeedback in the wild to elicit subconscious behavior change</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Haghighi, Nava.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Engineering and Management Program.</Keyword> <Keyword>System Design and Management Program.</Keyword> <Keyword>Integrated Design and Management Program.</Keyword> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In this thesis, I introduce Self-Interfaces as a method for creating behavior change. Self-Interfaces are interfaces that intuitively communicate relevant aspects of covert physiological signals through biofeedback to give the user insight into their behavior and assist them in creating behavior change. The human heartbeat is a good example of an intuitive and relevant haptic biofeedback; it does not distract and is only felt when the heart beats fast. My vision is to identify other covert physiological processes and instances in which they become useful, and augment our awareness of those signals in order to create behavior change. As a first case-study, I develop the Self-Interface for Electrodermal Activity (EDA), which is designed to help regulate attention and interest in users with Attention Deficit Hyperactivity Disorder (ADHD). EDA is a covert physiological signal correlated with high and low arousal affective states. Three studies were carried out to: 1. identify the design criteria for development of the EDA Self-Interface, 2. identify guidelines to reduce the cognitive load imposed by the haptic biofeedback signal, and 3. identify the aspects of the EDA that are relevant and insightful for the ADHD population. The insights from these studies contributed to the design and development of the EDA Self-Interface which has three components: EDA Sensor (Affectiva E4 Sensor), a wearable haptic biofeedback interface, and a phone app to process the EDA data and communicate it with the wearable interface. Lastly, I discuss the evaluation criteria for the EDA Self-Interface and propose a longitudinal study for such evaluation.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>