Increased strain on the hospitality industry following the COVID-19 pandemic and ensuing labor shortages has led to calls for new technologies such as smartwatches for understanding and improving work conditions for housekeepers, who are often from vulnerable and marginalized populations. In addition to concerns regarding how new technologies will be received by workers, questions remain as to whether metrics derived from smartwatches can usefully predict aspects of the worker experience, such as feelings of stress. We recruited 20 hotel housekeepers to wear smartwatch-based sensors during work for approximately 20 eight-hour shifts. In addition to pre- and post-participation surveys on workplace attitudes, participants provided daily stress ratings. Findings revealed increased technology acceptance and perceptions of organizational support following smartwatch use. Smartwatch metrics reliably predicted participant ratings of daily and overall stress. Theoretical implications for technology acceptance and practical implications for introducing technology into the workplace are discussed.

Augmented Reality (AR) tools have demonstrated considerable promise to enhance creative architectural design and support the retrofitting problem-solving processes through on-site daylighting visualization. AR’s capacity to integrate embodied motion enhances the non-expert’s understanding of the spatial characteristics and design ramifications within the built environment for complex facade design. Motion provides insights and increases the accessibility of retrofitting, encouraging more energy-efficient rework as opposed to complete building reconstruction. This study investigates the decision-making outcomes and cognitive-physical load implications of integrating a Building Information Modeling-driven AR system into the retrofitting design process and how movement is best leveraged to understand daylighting impacts. We conducted a study with 128 non-expert participants, who were asked to choose a window facade retrofit to improve an interior space. We analyze the effects of head movement, head rotations, and eye movements to understand how embodied motion improves overall objective performance across several daylighting and energy design metrics. We found no significant difference in the overall decision-making outcome between those who used an AR tool or a conventional desktop approach and that greater eye movement in AR was related to non-experts better balancing the complicated impacts facades have on daylight, aesthetics, and energy. This study indicates future expansion of AR retrofitting tools should encourage more eye movement.

Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.

Augmented Reality (AR) tools have shown significant potential in providing on-site visualization of Building Information Modeling (BIM) data and models for supporting construction evaluation, inspection, and guidance. Retrofitting existing buildings, however, remains a challenging task requiring more innovative solutions to successfully integrate AR and BIM. This study aims to investigate the impact of AR+BIM technology on the retrofitting training process and assess the potential for future on-site usage. We conducted a study with 64 non-expert participants, who were asked to perform a common retrofitting procedure of an electrical outlet installation using either an AR+BIM system or a standard printed blueprint documentation set. Our findings indicate that AR+BIM reduced task time significantly and improved performance consistency across participants, while also decreasing the physical and cognitive demands of the training. This study provides a foundation for augmenting future retrofitting construction research that can extend the use of [Formula: see text] technology, thus facilitating more efficient retrofitting of existing buildings. A video presentation of this article and all supplemental materials are available at https://github.com/DesignLabUCF/SENSEable_RetrofittingTraining.

We present the Robotic Modeling Assistant (RoMA), an interactive fabrication system providing a fast, precise, hands-on and in-situ modeling experience. As a designer creates a new model using RoMA AR CAD editor, features are constructed concurrently by a 3D printing robotic arm sharing the same design volume. The partially printed physical model then serves as a tangible reference for the designer as she adds new elements to her design. RoMA's proxemics-inspired handshake mechanism between the designer and the 3D printing robotic arm allows the designer to quickly interrupt printing to access a printed area or to indicate that the robot can take full control of the model to finish printing. RoMA lets users integrate real-world constraints into a design rapidly, allowing them to create well-proportioned tangible artifacts or to extend existing objects. We conclude by presenting the strengths and limitations of our current design.

The illumination and appearance of the solar/skydome is critical for many applications in computer graphics, computer vision, and daylighting studies. Unfortunately, physically accurate measurements of this rapidly changing illumination source are difficult to achieve, but necessary for the development of accurate physically-based sky illumination models and comparison studies of existing simulation models. To obtain baseline data of this time-dependent anisotropic light source, we design a novel acquisition setup to simultaneously measure the comprehensive illumination properties. Our hardware design simultaneously acquires its spectral, spatial, and temporal information of the skydome. To achieve this goal, we use a custom built spectral radiance measurement scanner to measure the directional spectral radiance, a pyranometer to measure the irradiance of the entire hemisphere, and a camera to capture high-dynamic range imagery of the sky. The combination of these computer-controlled measurement devices provides a fast way to acquire accurate physical measurements of the solar/skydome. We use the results of our measurements to evaluate many of the strengths and weaknesses of several sun-sky simulation models. We also provide a measurement dataset of sky illumination data for various clear sky conditions and an interactive visualization tool for model comparison analysis available at http://www.graphics.cornell.edu/resources/clearsky/.

There has been a recent paradigm shift in the computer animation industry with an increasing use of pre-recorded motion for animating virtual characters. A fundamental requirement to using motion capture data is an efficient method for indexing and retrieving motions. In this paper, we propose a flexible, efficient method for searching arbitrarily complex motions in large motion databases. Motions are encoded using keys which represent a wide array of structural, geometric and, dynamic features of human motion. Keys provide a representative search space for indexing motions and users can specify sequences of key values as well as multiple combination of key sequences to search for complex motions. We use a trie-based data structure to provide an efficient mapping from key sequences to motions. The search times (even on a single CPU) are very fast, opening the possibility of using large motion data sets in real-time applications.

In this paper we investigate the utility of an interactive, desktop-based virtual reality (VR) system for training personnel in hazardous working environments. Employing a novel software model, CRAM (Course Resource with Active Materials), we asked participants to learn a specific aircraft maintenance task. The evaluation sought to identify the type of familiarization training that would be most useful prior to hands on training, as well as after, as skill maintenance. We found that participants develop an increased awareness of hazards when training with stimulating technology - in particular (1) interactive, virtual simulations and (2) videos of an instructor demonstrating a task - versus simply studying (3) a set of written instructions. The results also indicate participants desire to train with these technologies over the standard written instructions. Finally, demographic data collected during the evaluation elucidates future directions for VR systems to develop a more robust and stimulating hazard training environment.

Abstract Aging and imperfections provide important visual cues for realism. We present a novel physically‐based approach for simulating the biological aging and decay process in fruits. This method simulates interactions between multiple processes. Our biologically‐derived, reaction‐diffusion model generates growth patterns for areas of fungal and bacterial infection. Fungal colony spread and propagation is affected by both bacterial growth and nutrient depletion. This process changes the physical properties of the surface of the fruit as well as its internal volume substrate. The fruit is physically simulated with parameters such as skin thickness and porosity, water content, flesh rigidity, ambient temperature, humidity, and proximity to other surfaces. Our model produces a simulation that closely mirrors the progression of decay in real fruits under similar parameterized conditions. Additionally, we provide a tool that allows artists to customize the input of the program to produce generalized fruit simulations.

Optimal heuristic searches such as A* search are commonly used for low-dimensional planning such as 2D path finding. These algorithms however, typically do not scale well to high-dimensional planning problems such as motion planning for robotic arms, computing motion trajectories for non-holonomic robotic vehicles and motion synthesis for humanoid characters. A recently developed randomized version of A* search, called R* search, scales to higher-dimensional planning problems by trading off deterministic optimality guarantees of A* for probabilistic sub-optimality guarantees. In this paper, we show that in addition to its scalability, R* lends itself well to a parallel implementation. In particular, we demonstrate how R* can be implemented on the GPU. On the theoretical side, the GPU version of R*, called R*GPU, preserves all the theoretical properties of R* including its probabilistic bounds on sub-optimality. On the experimental side, we show that R*GPU consistently produces lower cost solutions, scales better in terms of memory, and runs faster than R*. These results hold for both motion planning for a 6DOF robot arm planar as well as 2D path finding.