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Massachusetts Institute of Technology

Self-Interfaces : utilizing real-time biofeedback in the wild to elicit subconscious behavior change

Abstract

dc:description.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.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Engineering and Management Program
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Haghighi, Nava.
Advisor dc:contributor.advisor
  • Arvind Satyanarayan.

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • 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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/132823
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/132823

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Haghighi, Nava.. Self-Interfaces : utilizing real-time biofeedback in the wild to elicit subconscious behavior change. Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/132823