Full paper: Input Accessibility: A Large Dataset and Summary Analysis of Age, Motor Ability and Input Performance 

ABSTRACT

Age and motor ability are well-known to impact input performance. Past work examining these factors, however, has tended to focus on samples of 20-40 participants and has binned participants into a small set of age groups (e.g., “younger” vs. “older”). To foster a more nuanced understanding of how age and motor ability impact input performance, this short paper contributes: (1) a dataset from a large-scale study that captures input performance with a mouse and/or touchscreen from over 700 participants, as well as (2) summary analysis of a subset of 318 participants who range in age from 18 to 83 years old and of whom 53% reported a motor impairment. The analysis demonstrates the continuous relationship between age and input performance for users with and without motor impairments, but also illustrates that knowing a user's age and self-reported motor ability should not lead to assumptions about their input performance. The dataset, which contains mouse and touchscreen input traces, should allow for further exploration by other researchers.

Full paper: Living Disability Theory: Reflections on Access, Research, and Design

ABSTRACT

Accessibility research and disability studies are intertwined fields focused on, respectively, building a world more inclusive of people with disability and understanding and elevating the lived experiences of disabled people. Accessibility research tends to focus on creating technology related to impairment, while disability studies focuses on understanding disability and advocating against ableist systems. Our paper presents a reflexive analysis of the experiences of three accessibility researchers and one disability studies scholar. We focus on moments when our disability was misunderstood and causes such as expecting clearly defined impairments. We derive three themes: ableism in research, oversimplification of disability, and human relationships around disability. From these themes, we suggest paths toward more strongly integrating disability studies perspectives and disabled people into accessibility research.

Full paper: SoundWatch: Exploring Smartwatch-based Deep Learning Approaches to Support Sound Awareness for Deaf and Hard of Hearing Users

ABSTRACT

Smartwatches have the potential to provide glanceable, always-available sound feedback to people who are deaf or hard of hearing. In this paper, we present a performance evaluation of four low-resource deep learning sound classification models: MobileNet, Inception, ResNet-lite, and VGG-lite across four device architectures: watch-only, watch+phone, watch+phone+cloud, and watch+cloud. While direct comparison with prior work is challenging, our results show that the best model, VGG-lite, performed similar to the state of the art for non-portable devices with an average accuracy of 81.2% (SD=5.8%) across 20 sound classes and 97.6% (SD=1.7%) across the three highest-priority sounds. For device architectures, we found that the watch+phone architecture provided the best balance between CPU, memory, network usage, and classification latency. Based on these experimental results, we built and conducted a qualitative lab evaluation of a smartwatch-based sound awareness app, called SoundWatch (Figure 1), with eight DHH participants. Qualitative findings show support for our sound awareness app but also uncover issues with misclassifications, latency, and privacy concerns. We close by offering design considerations for future wearable sound awareness technology.