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Background: Telework has benefits for many people with disabilities. The pandemic may create new employment opportunities for people with disabilities by increasing employer acceptance of telework, but this crucially depends on the occupational structure. Objective: We compare people with and without disabilities in the expansion of telework as the pandemic began, and the evolution of telework during the pandemic. Methods: We use U.S. data from the American Community Survey from 2008 to 2020 and the Current Population Survey over the May 2020 to April 2022 period. Prevalence and trends are analyzed using linear probability and multinomial logit regressions. Results: While workers with disabilities were more likely than those without disabilities to telework before the pandemic, they were less likely to telework during the pandemic. The occupational distribution accounts for most of this difference. Tight labor markets, as measured by state unemployment rates, particularly favor people with disabilities obtaining telework jobs. While people with cognitive/mental health and mobility impairments were the most likely to telework during the pandemic, tight labor markets especially favor

Nature plays a crucial role in human health and well-being, but little is known about how blind people experience and relate to it. We conducted a survey of nature relatedness with blind (N=20) and sighted (N=20) participants, along with in-depth interviews with 16 blind participants, to examine how blind people engage with nature and the factors shaping this engagement. Our survey results revealed lower levels of nature relatedness among blind participants compared to sighted peers. Our interview study further highlighted: 1) current practices and challenges of nature engagement, 2) attitudes and values that shape engagement, and 3) expectations for assistive technologies that support safe and meaningful engagement. We also provide design implications to guide future technologies that support nature engagement for blind people. Overall, our findings illustrate how blind people experience nature beyond vision and lay a foundation for technologies that support inclusive nature engagement.

Group interactions are essential to social functioning, yet effective engagement relies on the ability to recognize and interpret visual cues, making such engagement a significant challenge for blind people. In this paper, we investigate how a mobile robot can support group interactions for blind people. We used the scenario of a guided tour with mixed-visual groups involving blind and sighted visitors. Based on insights from an interview study with blind people (n=5) and museum experts (n=5), we designed and prototyped a robotic system that supported blind visitors to join group tours. We conducted a field study in a science museum where each blind participant (n=8) joined a group tour with one guide and two sighted participants (n=8). Findings indicated users' sense of safety from the robot's navigational support, concerns in the group participation, and preferences for obtaining environmental information. We present design implications for future robotic systems to support blind people's mixed-visual group participation.

Young people are major consumers of Augmented Reality (AR) tools like Pokémon GO, but they rarely engage in creating these experiences. Creating with technology gives young people a platform for expressing themselves and making social connections. However, we do not know what young people want to create with AR, as existing AR authoring tools are largely designed for adults. To investigate the requirements for an AR authoring tool, we ran eight design workshops with 17 young people in Argentina and the United States that centered on young people's perspectives and experiences. We identified four ways in which young people want to create with} AR, and contribute the following design implications for designers of AR authoring tools for young people: (1) Blending imagination into AR scenarios to preserve narratives, (2) Making traces of actions visible to foster social presence, (3) Exploring how AR artifacts can serve as invitations to connect with others, and (4) Leveraging information asymmetry to encourage learning about the physical world.

This article focuses on the concept of self-determination and the design and validation of digital tools intended to promote the self-determination of vulnerable people. Self-determination is an essential skill for carrying out daily activities. But in certain situations, and for certain populations, self-determination is lacking, which leads to the inability to live an independent life and in favorable conditions of well-being and health. In recent years, self-determination enhancing technologies have been developed and used to promote independent living among people with self-determination disorders. We will illustrate the main digital tools to support self-determination developed for two populations of people suffering from self-determination disorders: people with an intellectual disability and people with an autism spectrum disorder. The ability of these digital assistants to improve the comfort of life of these people will also be presented and discussed.

LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work, we review all research mentioning LGBTQ+ people across the HCI venues of CHI, CSCW, DIS, and TOCHI. Since 2014, we find a linear growth in the number of papers substantially about LGBTQ+ people and an exponential increase in the number of mentions. Research about LGBTQ+ people tends to center experiences of being politicized, outside the norm, stigmatized, or highly vulnerable. LGBTQ+ people are typically mentioned as a marginalized group or an area of future research. We identify gaps and opportunities for (1) research about and (2) the discussion of LGBTQ+ in HCI and provide a dataset to facilitate future Queer HCI research.

Capturing the diversity of people in images is challenging: recent literature tends to focus on diversifying one or two attributes, requiring expensive attribute labels or building classifiers. We introduce a diverse people image ranking method which more flexibly aligns with human notions of people diversity in a less prescriptive, label-free manner. The Perception-Aligned Text-derived Human representation Space (PATHS) aims to capture all or many relevant features of people-related diversity, and, when used as the representation space in the standard Maximal Marginal Relevance (MMR) ranking algorithm, is better able to surface a range of types of people-related diversity (e.g. disability, cultural attire). PATHS is created in two stages. First, a text-guided approach is used to extract a person-diversity representation from a pre-trained image-text model. Then this representation is fine-tuned on perception judgments from human annotators so that it captures the aspects of people-related similarity that humans find most salient. Empirical results show that the PATHS method achieves diversity better than baseline methods, according to side-by-side ratings from human annotators.

Independent navigation in unfamiliar environments remains a major challenge for blind and visually impaired individuals, despite the availability of assistive technologies. This paper presents the results of a fully accessible online survey investigating navigation experiences, challenges, and technology preferences among people with visual impairments worldwide. The survey was distributed through individuals and organizations supporting visually impaired communities. Our results indicate that smartphone-based applications are the most used digital navigation aids, while a substantial proportion of participants report not using any assistive navigation technology due to cost, accessibility, or usability barriers. Participants reported persistent difficulties in obstacle detection, wayfinding, and navigation in complex environments. Despite a widespread focus on smartphone-based solutions, they expressed a clear preference for wearable and hands-free systems, highlighting a gap between current technology use and user needs. The findings provide a user-centered overview of navigation needs and offer insights into the design and evaluation of future assistive navigation systems.

More than a million people in the UK suffer from frailty or dementia, which severely compromise their ability to travel in urban environments. This paper presents SafeStep, an AI-driven travel system that assists elderly users with their journeys. At the core of SafeStep is a novel travel graph representation, which integrates route planning with predictive modelling. For each stage of a journey, the system (i) generates personalized failure scenarios using a combi-nation of LLMs and the Anticip8 behavioral prediction engine, (ii) proposes targeted interventions, and (iii) estimates the impact of interventions on out-come probabilities. This enables SafeStep to select interventions that maximize the likelihood of the person reaching their destination. SafeStep was evaluated through experiments on travel graph generation and a field study involving 26 real-world journeys. Results showed that combining Anticip8 for failure pre-diction with GPT-based models for intervention evaluation yields the most re-liable performance. User feedback indicated that SafeStep improves confidence and perceived safety during travel, although interface usability needs to be im-proved for the target demo

The topic of this final qualification work was chosen due to the importance of developing robotic systems designed to assist people with disabilities. Advances in robotics and automation technologies have opened up new prospects for creating devices that can significantly improve the quality of life for these people. In this context, designing a robotic hand with a control system adapted to the needs of people with disabilities is a major scientific and practical challenge. This work addresses the problem of developing and manufacturing a four-degree-of-freedom robotic hand suitable for practical manipulation. Addressing this issue requires a comprehensive approach, encompassing the design of the hand's mechanical structure, the development of its control system, and its integration with a technical vision system and software based on the Robot Operating System (ROS).

Many researchers have developed VR systems for people with visual impairments by using various audio feedback techniques. However, there has been much less study of collaborative VR systems in which people with visual impairments and people with able-body can participate together. Therefore, we developed a VR showdown game which is similar to a real Showdown game in which two players can play together in the same virtual environment. We incorporate auditory distance perception using the HRTF (Head Related Transform Function) based on a spatial position in VR. We developed two modes in the showdown game. One is the PVA (Player vs. Agent) mode in which people with visual impairments can play alone and the PVP (Player vs. Player) mode in which people with visual impairments can play with another player in the network environment. We conducted our user studies by comparing the performances of people with visual impairments and people with able-body. The user study results show that people with visual impairments won 67.6% of the games when competing against people with able-body. This paper reports an example of a collaborative VR system for people with visual impairments and also desi

Painting and music therapy approaches can help to foster social interaction for autistic people. However, the tools sometimes lack of flexibility and fail to keep people's attention. Unknowns also remain about the effect of combining these approaches. Though, very few studies have investigated how Multisensory Environments (MSEs) could help to address these issues. This paper presents the design of a full-body music and painting activity called "MusicTraces" which aims to foster collaboration between people with moderate to severe learning disabilities and complex needs, and in particular autism, within an MSE. The co-design process with caregivers and people neurodevelopmental conditions is detailed, including a workshop, the initial design, remote iterations, and a design critique.

Disabled people experience many barriers in daily life, but non-disabled people rarely pause to reflect and engage in joint action to advocate for access. In this demo, we explore the potential of Virtual Reality (VR) to sensitize non-disabled people to barriers in the built environment. We contribute a VR simulation of a major traffic hub in Karlsruhe, Germany, and we employ visual embellishments and animations to showcase barriers and potential removal strategies. Through our work, we seek to engage users in conversation on what kind of environment is accessible to whom, and what equitable participation in society requires. Additionally, we aim to expand the understanding of how VR technology can promote reflection through interactive exploration.

More and more smart devices enter our homes. Often these devices come with a variety of sensors, mostly simple sensors, e.g., for light, temperature, humidity or motion. And they all collect data. While it is data of the home environment it is also data of domestic life in the home. Thus it is data of the people and by the people in the home capturing their presence, arrival and departure, typical domestic activities, bad habits, health status etc. Based on previous as well as ongoing research we know that people are actually able to make sense of simple sensor data and that they will make use of it for their own purposes. Simple sensors, when critically reflected, are often only "simple" in a technical sense. The unreflected design and use of these sensors can easily lead to unintended implications, i.e. for privacy. However, it may not even need a Big Brother or data experts or AI to make the data of these sensors sensitive, e.g., if used for lateral surveillance within families. Often unintended but wicked implications emerge despite good intentions, such as improving efficiency or energy saving through collecting sensor data. Thus sensor data from the home is actually data of/b

Data has transformative potential to empower people with Intellectual and Developmental Disabilities (IDD). However, conventional data visualizations often rely on complex cognitive processes, and existing approaches for day-to-day analysis scenarios fail to consider neurodivergent capabilities, creating barriers for people with IDD to access data and leading to even further marginalization. We argue that visualizations could be an equalizer for people with IDD to participate in data-driven conversations. Drawing on preliminary research findings and our experiences working with people with IDD and their data, we introduce and expand on the concept of cognitively accessible visualizations, unpack its meaning and roles in increasing IDD individuals' access to data, and discuss two immediate research objectives. Specifically, we argue that cognitively accessible visualizations should support people with IDD in personal data storytelling for effective self-advocacy and self-expression, and balance novelty and familiarity in data design to accommodate cognitive diversity and promote inclusivity.

Collective behavior of people in large groups and emergent crowd dynamics can have dangerous and disastrous results when panic is introduced. These events can be caused by emergency situations such as fires in a large building or a stampeding effect when people are rushing in a densely packed area. In this paper, we will use an agent-based modeling approach to simulate different evacuation events in an attempt to understand what is the most efficient scenario. Specifically, we will focus on how people with disabilities are impacted by chosen parameters during an emergency evacuation. We chose an ABM to simulate this because we want to specify specific roles for different "agents" in our model. Specifically, we will focus on the influence of people with disabilities on crowd dynamics and the optimal exits. Does the placement of seating for people with disabilities affect the time it takes for the last person to exit the building? What effect does poor signage have on the time it takes for able-bodied and people with disabilities to exit safely? What happens if some people do not know about alternative exits in their panicked state? Using our agent-based model, we will investigate th

People search is an important topic in information retrieval. Many previous studies on this topic employed social networks to boost search performance by incorporating either local network features (e.g. the common connections between the querying user and candidates in social networks), or global network features (e.g. the PageRank), or both. However, the available social network information can be restricted because of the privacy settings of involved users, which in turn would affect the performance of people search. Therefore, in this paper, we focus on the privacy issues in people search. We propose simulating different privacy settings with a public social network due to the unavailability of privacy-concerned networks. Our study examines the influences of privacy concerns on the local and global network features, and their impacts on the performance of people search. Our results show that: 1) the privacy concerns of different people in the networks have different influences. People with higher association (i.e. higher degree in a network) have much greater impacts on the performance of people search; 2) local network features are more sensitive to the privacy concerns, espec

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing them. This enables us to impose much stronger constraints encoding the conservation of the number of people. As a result, it significantly boosts performance without requiring a more complex architecture. Furthermore, it allows us to exploit the correlation between people flow and optical flow to further improve the results. We also show that leveraging people conservation constraints in both a spatial and temporal manner makes it possible to train a deep crowd counting model in an active learning setting with much fewer annotations. This significantly reduces the annotation cost while still leading to similar performance to the full supervision case.

Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern. However, existing methods rely heavily on supervised learning using large quantities of annotated image samples, which are more costly to collect for important people detection than for individual entity recognition (eg, object recognition). To overcome this problem, we propose learning important people detection on partially annotated images. Our approach iteratively learns to assign pseudo-labels to individuals in un-annotated images and learns to update the important people detection model based on data with both labels and pseudo-labels. To alleviate the pseudo-labelling imbalance problem, we introduce a ranking strategy for pseudo-label estimation, and also introduce two weighting strategies: one for weighting the confidence that individuals are important people to strengthen the learning on important people and the other for neglecting noisy unlabelled images (ie, images without any important people). We have collected two large-scale datasets for evaluation. The extensive experim