Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Computer Science


Computer Science and Statistics

First Advisor

Krishna Venkatasubramanian


Computing device authentication, the process of proving one's identity to a computing device (e.g., laptop, tablet, smart phone, etc.), is critical to protecting the sensitive data these devices contain. However, computing device authentication often makes assumptions about the user's ability to perform complex tasks with their arms, hands, and fingers. For example, entering complex passwords, or accurately positioning a camera for facial recognition. This can create barriers for people with upper extremity impairment (UEI) who have reduced strength, accuracy, dexterity, speed, and/or range of motion in their shoulders, upper arms, forearms, hands, and/or fingers. My goal is to design new, more accessible computing device authentication for people with UEI.

I detail my approach to address this goal in four core steps. I first explore the current state of computing device authentication for people with UEI by conducting 8 semi-structured interviews with people with UEI. I then follow up to determine what impacts the COVID-19 pandemic had on the computing device use of people with UEI by conducting 6 semi-structured interviews with people with UEI. Using the results from these interviews, I propose two novel forms of authentication designed for people with UEI: (1) ballistocardiogram-based authentication, and (2) ALPACA, a recognition-based graphical authentication method. I evaluate both of these methods and show their potential viability as secure and accessible authentication methods for people with UEI. Based on my findings throughout these four manuscripts, I suggest seven areas of future research.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.



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