Event Title

Stress Modelling for participants with Autism Spectrum Disorder

Co-Author(s)

Pranger, Cody; Wu Liang, Phillip

Research Mentor(s)

Ahmed, Shameem

Description

The main goal of this research project is to collect qualitative and quantitative data from college students who are on and off the autistic spectrum so as to have a measurable means of determining stress level. The study collects information from college students through various means including interviews, small surveys given through a smart phone application, and a Fitbit Charge 2 fitness tracker. All of this data is accumulated in hopes that the information can be used to make a machine learning model that can predict stress given only this information.

Document Type

Event

Start Date

15-5-2019 9:00 AM

End Date

15-5-2019 5:00 PM

Location

Carver Gym (Bellingham, Wash.)

Department

Computer Science

Genre/Form

student projects, posters

Subjects – Topical (LCSH)

College students--Mental health; Autistic people--Mental health; Stress (Psychology)--Testing; Stress (Physiology)--Testing

Type

Image

Rights

Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.

Language

English

Format

application/pdf

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May 15th, 9:00 AM May 15th, 5:00 PM

Stress Modelling for participants with Autism Spectrum Disorder

Carver Gym (Bellingham, Wash.)

The main goal of this research project is to collect qualitative and quantitative data from college students who are on and off the autistic spectrum so as to have a measurable means of determining stress level. The study collects information from college students through various means including interviews, small surveys given through a smart phone application, and a Fitbit Charge 2 fitness tracker. All of this data is accumulated in hopes that the information can be used to make a machine learning model that can predict stress given only this information.