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Applying the principles of trauma-informed care when designing apps can improve accessibility for people with intellectual and developmental disabilities. 

Current computing device authentication often presents accessibility barriers for people withupper extremity impairments (UEI). In this article, we present a framework calledAccessible image-Association-based Authentication for Computing devices (A3C), a novel recognition-based graphical authentication framework specifically designed for people with UEI to authenticate to their computing devices. A3C requires users to provide a set of primary images the user knows that are recognizable to them and subsequently associate each primary image with a secondary image. To evaluate the efficacy of the A3C framework, we instantiated the framework by implementing a version of A3C calledA3C-FA, which uses images of faces of people the user knows as the primary image and animal images as the secondary image. We then performed three studies to evaluate A3C-FA: a shoulder-surfing attack study (N\(=\)319), a close-adversary attack study (N\(=\)268), and a usability study with people with UEI (N\(=\)14). We found that A3C was robust against both shoulder-surfing and close-adversary attacks. We also performed a detailed study to evaluate the accessibility of A3C-FA. Our participants reported that A3C-FA was more usable and more secure than the authentication approaches with which they were familiar. Based on these findings, we suggest four areas of future research to further improve the design of the A3C framework. 

Recent years have seen a proliferation of security-focused smart home devices (SSHDs). SSHDs, such as smart locks and cameras, are designed to accomplish critical tasks, such as protecting one’s home and property. However, their use by and for people with disabilities (PwD) has not been broadly investigated. To explore the state of SSHD use by PwD, we collected 114,871 amazon.com reviews for popular SSHDs and created a data set of reviews pertaining to PwD. We performed a broad analysis of the reviews in this data set and found that the presence of SSHDs empowered PwD to secure their domiciles independently. Further, caregivers used SSHDs to monitor PwD, ostensibly for the latter’s safety, albeit without explicit consent. Moreover, we also found that SSHDs have several drawbacks that impose various barriers of use on PwD. We analyze the significance of these findings and suggest five future research opportunities for SSHD design. 

In this paper, we explore how computing device use by people with upper extremity impairment (UEI) was affected by COVID-19. Someone with UEI has reduced use of their shoulders, upper arms, forearms, hands, and/or fingers. We conducted six (6) semi-structured interviews with participants with UEI in the US. We found that people with UEI increased computing device use during COVID-19 not only for remote interactions but also in person. Additionally, social distancing for COVID-19 safety created the need for new assistive technology (AT), authentication requirements, and communication platforms, which introduced their own accessibility barriers. We also found that personal protective equipment (PPE) created new barriers during computing device use, which often caused people with UEI to choose COVID-19 safety over the usability of their computing devices. Based on these findings, we describe future opportunities to make computing devices more accessible for people with UEI to manage the shifts in computing device use introduced by COVID-19. 

Authentication has become increasingly ubiquitous for controlling access to personal computing devices (e.g., laptops, tablets, and smartphones). In this paper, we aim to understand the authentication process used by people with upper extremity impairment (UEI). A person with UEI lacks range of motion, strength, endurance, speed, and/or accuracy associated with arms, hands, or fingers. To this end, we conducted semi-structured interviews with eight (8) adults with UEI about their use of authentication for their personal computing devices. We found that our participants primarily use passwords and PINs as a verification credential during authentication. We found the process of authentication to have several accessibility issues for our participants. Consequently, our participants implemented a variety of workarounds that prioritized usability over security throughout the authentication process. Based on these findings, we present six broad subareas of research that should be explored in order to create more accessible authentication for people with UEI. 

In recent years, people with upper extremity impairment (UEI) have been using wearable Internet of Things (wIoT) devices like head-mounted devices (HMDs) for a variety of purposes such as rehabilitation, assistive technology, and gaming. Often such wIoT devices collect and display sensitive information such as information related to medical care and rehabilitation. It is therefore crucial that HMDs can authenticate the person wearing them so that appropriate access control can be implemented for the sensitive information they manage. In this paper, we explore a new authentication approach for people with upper extremity impairment (UEI) for wIoT devices head-mounted devices (HMDs). The approach works by leveraging ballistocardiograms - representations of the cardiac rhythm - derived from an accelerometer and a gyroscope, mounted on an HMD for authentication. The derived ballistocardiograms are then fed into six participant-specific convolutional neural networks (CNNs) which act as our authentication models. Analysis of our approach shows its viability. Using data from 6 participants with UEI (and 22 able-bodied participants, for evaluation), we show that we can authenticate a participant in 4 seconds with an average equal error rate of 4.02% and 10.02%, immediately after training and ~2 months later, respectively.