Divers face numerous physiological hazards that can lead to diving disorders, yet the underlying mechanisms remain poorly understood due to limitations in monitoring technologies capable of functioning underwater. To bridge this gap, it is necessary to critically evaluate novel monitoring technologies and develop approaches to determine their suitability for near real-time physiological assessment. Gold standard devices are typically used to validate new physiological sensors under controlled normobaric conditions, where their accuracy benefits from stable, low-interference environments. However, the absence of underwater gold standards and the complexity of underwater conditions make it difficult to determine whether measurement discrepancies arise from true physiological changes or sensor variability. Given the wide range of physiological responses elicited by diving, it is essential to deconstruct the underwater experience into its constituent factors to better isolate their individual effects. This article presents a comprehensive methodology for evaluating the performance of a novel physiological sensor designed for both land-based and underwater environments. To this end, human subject testing was conducted across three experimental environments: 1. dry normobaric testing, 2. dry hyperbaric testing, and 3. shallow water immersion. To facilitate these evaluations, a custom data acquisition platform was developed on Robot Operating System 2 (ROS 2), enabling coordinated synchronization of multiple heterogeneous data streams. This approach offers a scalable and reproducible framework for validating physiological monitoring technologies.

Visualizations support critical decision making in domains like health risk communication. This is particularly important for those at higher health risks and their care providers, allowing for better risk interpretation which may lead to more informed decisions. However, the kinds of visualizations used to represent data may impart biases that influence data interpretation and decision making. Both continuous representations using bar charts and discrete representations using icon arrays are pervasive in health risk communication, but express the same quantities using fundamentally different visual paradigms. We conducted a series of studies to investigate how bar charts, icon arrays, and their layout (juxtaposed, explicit encoding, explicit encoding plus juxtaposition) affect the perception of value comparison and subsequent decision-making in health risk communication. Our results suggest that icon arrays and explicit encoding combined with juxtaposition can optimize for both accurate difference estimation and perceptual biases in decision making. We also found misalignment between estimation accuracy and decision making, as well as between low and high literacy groups, emphasizing the importance of tailoring visualization approaches to specific audiences and evaluating visualizations beyond perceptual accuracy alone. This research contributes empirically-grounded design recommendations to improve comparison in health risk communication and support more informed decision-making across domains.

As scientific research in chemistry, materials science, and applied sciences becomes increasingly complex and data-driven, there is a growing need for efficient, scalable, and flexible automation to accelerate discoveries and reduce human burden and error in laboratories. We introduce the Experiment Orchestration System (EOS), an open-source software framework and runtime offering a comprehensive foundation for laboratory automation. EOS offers an extensible framework allowing users to define labs, devices, tasks, experiments, and optimization criteria using YAML and Python plugins, and also offers a distributed runtime for managing and executing automation. EOS has a central orchestrator that communicates with and controls laboratory equipment to execute tasks. EOS implements autonomous experiment campaigns, parameter optimization, task scheduling, result aggregation, and more. By providing a common infrastructure for laboratory automation, EOS aims to reduce automation implementation barriers and accelerate discoveries in science laboratories.

Augmented reality (AR) offers promising opportunities to support movement-based activities, such as personal training or physical therapy, with real-time, spatially-situated visual cues. While many approaches leverage AR to guide motion, existing design guidelines focus on simple, upper-body movements within the user's field of view. We lack evidence-based design recommendations for guiding more diverse scenarios involving movements with varying levels of visibility and direction. We conducted an experiment to investigate how different visual encodings and perspectives affect motion guidance performance and usability, using three exercises that varied in visibility and planes of motion. Our findings reveal significant differences in preference and performance across designs. Notably, the best perspective varied depending on motion visibility and showing more information about the overall motion did not necessarily improve motion execution. We provide empirically-grounded guidelines for designing immersive, interactive visualizations for motion guidance to support more effective AR systems.

Virtual Reality(VR) systems can capture detailed motion data for full-body tracking. However, little is known about what personal information observers perceive from full-body movements. Perceived personal information such as gender, physical attributes, health conditions, and cultural background could enable discrimination, bias, or harassment. We conducted a study to investigate what personal traits observers perceive from full-body VR motion data. Observers frequently perceived gender, physical attributes, and emotional states within just 10 seconds of exposure, with initial movement observations leading to cascading perceptions about increasingly specific personal characteristics including occupation and geographic origin. These results highlight how full-body motion data can expose personal information through natural human motion interpretation, which may impede user adoption and raise significant privacy concerns.

Physical therapy (PT) plays a crucial role in muscle injury recovery, but people struggle to adhere to and perform PT exercises correctly from home. To support challenges faced with in-home PT, augmented reality (AR) holds promise in enhancing patient's engagement and accuracy through immersive interactive visualizations. However, effectively leveraging AR requires a better understanding of patient needs during injury recovery. Through interviews with six individuals undergoing physical therapy, this paper introduces user-centered design considerations integrating AR and body motion data to enhance in-home PT for injury recovery. Our findings identify key challenges and propose design variables for future body-based visualizations of body motion data for PT.

People with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients' motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions.

We envision a future in which telepresence is available to users anytime and anywhere, enabled by sensors and displays embedded in accessories worn daily, such as watches, jewelry, belt buckles, shoes, and eyeglasses. We present a collaborative approach to 3D reconstruction that combines a set of IMUs worn by a target person with an external view from another nearby person wearing an AR headset, used for estimating the target person's body pose and reconstructing their appearance, respectively.