Existing pose estimation models perform poorly on wheelchair users due to a lack of representation in training data. We present a data synthesis pipeline to address this disparity in data collection and subsequently improve pose estimation performance for wheelchair users. Our configurable pipeline generates synthetic data of wheelchair users using motion capture data and motion generation outputs simulated in the Unity game engine. We validated our pipeline by conducting a human evaluation, investigating perceived realism, diversity, and an AI performance evaluation on a set of synthetic datasets from our pipeline that synthesized different backgrounds, models, and postures. We found our generated datasets were perceived as realistic by human evaluators, had more diversity than existing image datasets, and had improved person detection and pose estimation performance when fine-tuned on existing pose estimation models. Through this work, we hope to create a foothold for future efforts in tackling the inclusiveness of AI in a data-centric and human-centric manner with the data synthesis techniques demonstrated in this work. Finally, for future works to extend upon, we open source all code in this research and provide a fully configurable Unity Environment used to generate our datasets. In the case of any models we are unable to share due to redistribution and licensing policies, we provide detailed instructions on how to source and replace said models. All materials can be found at https://github.com/hilab-open-source/wheelpose.

Extended reality (XR) has the potential for seamless user interface (UI) transitions across people, objects, and environments. However, the design space, applications, and common practices of 3D UI transitions remain underexplored. To address this gap, we conducted a need-finding study with 11 participants, identifying and distilling a taxonomy based on three types of UI placements — affixed to static, dynamic, or self entities. We further surveyed 113 commercial applications to understand the common practices of 3D UI mobility control, where only 6.2% of these applications allowed users to transition UI between entities. In response, we built interaction prototypes to facilitate UI transitions between entities. We report on results from a qualitative user study (N=14) on 3D UI mobility control using our FingerSwitches technique, which suggests that perceived usefulness is affected by types of entities and environments. We aspire to tackle a vital need in UI mobility within XR.

Acquiring accessibility information about unfamiliar places in advance is essential for wheelchair users to make better decisions about physical visits. Today’s assessment approaches such as phone calls, photos/videos, or 360° virtual tours often fall short of providing the specific accessibility details needed for individual differences. For example, they may not reveal crucial information like whether the legroom underneath a table is spacious enough or if the spatial configuration of an appliance is convenient for wheelchair users. In response, we present Embodied Exploration, a Virtual Reality (VR) technique to deliver the experience of a physical visit while keeping the convenience of remote assessment. Embodied Exploration allows wheelchair users to explore high-fidelity digital replicas of physical environments with themselves embodied by avatars, leveraging the increasingly affordable VR headsets. With a preliminary exploratory study, we investigated the needs and iteratively refined our techniques. Through a real-world user study with six wheelchair users, we found Embodied Exploration is able to facilitate remote and accurate accessibility assessment. We also discuss design implications for embodiment, safety, and practicality.

Augmented reality (AR) and virtual reality (VR) technologies create exciting new opportunities for people to interact with computing resources and information. Less exciting is the need for holding hand controllers, which limits applications that demand expressive, readily available interactions. Prior research investigated freehand AR/VR input by transforming the user’s body into an interaction medium. In contrast to previous work that has users’ hands grasp virtual objects, we propose a new interaction technique that lets users’ hands become virtual objects by imitating the objects themselves. For example, a thumbs-up hand pose is used to mimic a joystick. We created a wide array of interaction designs around this idea to demonstrate its applicability in object retrieval and interactive control tasks. Collectively, we call these interaction designs Hand Interfaces. From a series of user studies comparing Hand Interfaces against various baseline techniques, we collected quantitative and qualitative feedback, which indicates that Hand Interfaces are effective, expressive, and fun to use.

Force sensing has been a key enabling technology for a wide range of interfaces such as digitally enhanced body and world surfaces for touch interactions. Additionally, force often contains rich contextual information about user activities and can be used to enhance machine perception for improved user and environment awareness. To sense force, conventional approaches rely on contact sensors made of pressure-sensitive materials such as piezo films/discs or force-sensitive resistors. We present ForceSight, a non-contact force sensing approach using laser speckle imaging. Our key observation is that object surfaces deform in the presence of force. This deformation, though very minute, manifests as observable and discernible laser speckle shifts, which we leverage to sense the applied force. This non-contact force-sensing capability opens up new opportunities for rich interactions and can be used to power user-/environment-aware interfaces. We first built and verified the model of laser speckle shift with surface deformations. To investigate the feasibility of our approach, we conducted studies on metal, plastic, wood, along with a wide variety of materials. Additionally, we included supplementary tests to fully tease out the performance of our approach. Finally, we demonstrated the applicability of ForceSight with several demonstrative example applications.