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paper on XR conference

Two papers were presented and published at the 2024 International Conference on eXtended Reality. XR Salento 2024.

Tang, Ming, Mikhail Nikolaenko, Evv Boerwinkle, Samuel Obafisoye, Aayush Kumar, Mohsen Rezayat, Sven Lehmann, and Tamara Lorenz. “Evaluation of the Effectiveness of Traditional Training vs. Immersive Training: A Case Study of Building Safety & Emergency Training.” Paper presented at the International Conference on eXtended Reality (XR SALENTO 2024), Lecce, Italy, September 4-9, 2024. The paper is published in the Springer Link proceeding book

Virtual Reality (VR) has revolutionized training across healthcare, manufacturing, and service sectors by offering realistic simulations that enhance engagement and knowledge retention. However, assessments that allow for evaluation of the effectiveness of VR training are still sparse. Therefore, we examine VR’s effectiveness in emergency preparedness and building safety, comparing it to traditional training methods. The goal is to evaluate the impact of the unique opportunities VR enables on skill and knowledge development, using digital replicas of building layouts for immersive training experiences. To that end, the research evaluates VR training’s advantages and develops performance metrics by comparing virtual performance with actions in physical reality, using wearable tech for performance data collection and surveys for insights. Participants, split into VR and online groups, underwent a virtual fire drill to test emergency response skills. Findings indicate that VR training boosts urgency and realism perception despite similar knowledge and skill acquisition after more traditional lecture-style training. VR participants reported higher stress and greater effectiveness, highlighting VR’s immersive benefits. The study supports previous notions of VR’s potential in training while also emphasizing the need for careful consideration of its cognitive load and technological demands.

 

Tang, M., Nored, J., Anthony, M., Eschmann, J., Williams, J., Dunseath, L. (2024). VR-Based Empathy Experience for Nonprofessional Caregiver Training. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2024. Lecture Notes in Computer Science, vol 15028. Springer, Cham. https://doi.org/10.1007/978-3-031-71704-8_28 

This paper presents the development of a virtual reality (VR) system designed to simulate various caregiver training scenarios, with the aim of fostering empathy by providing visual and emotional representations of the caregiver’s experience. The COVID-19 pandemic has increased the need for family members to assume caregiving roles, particularly for older adults who are at high risk for severe complications and death. This has led to a significant reduction in the availability of qualified home health workers. More than six million people aged 65 and older require long-term care, and two-thirds of these individuals receive all their care exclusively from family caregivers. Many caregivers are unprepared for the physical and emotional demands of caregiving, often exhibiting clinical signs of depression and higher stress levels.

The VR system, EVRTalk, developed by a multi-institutional team, addresses this gap by providing immersive training experiences. It incorporates theories of empathy and enables caregivers to switch roles with care recipients, navigating common scenarios such as medication management, hallucinations, incontinence, end-of-life conversations, and caregiver burnout. Research demonstrates that VR can enhance empathy, understanding, and communication skills among caregivers. The development process included creating believable virtual characters and interactive scenarios to foster empathy and improve caregiving practices. Initial evaluations using surveys showed positive feedback, indicating that VR training can reduce stress and anxiety for caregivers and improve care quality.

Future steps involve using biofeedback to measure physiological responses and further investigating the ethical implications of VR in caregiving training. The ultimate goal is to deploy VR training in homes, providing family caregivers with the tools and knowledge to manage caregiving responsibilities more effectively, thereby enhancing the quality of life for both caregivers and care recipients.

 

Poster in AHFE conference

Nancy Daraiseh, Aaron Vaughn, Ming Tang, Mikhail Nikolaenko, Madeline Aeschbury, Alycia Bachtel, Camryn Backman, Chunyan Liu, Maurizio Macaluso . Using Virtual Reality to Enhance Behavioral Staff Training for Interacting with Aggressive Psychiatric Patients. Poster. The 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). Nice, France, July 24-27, 2024.

Objective: To conduct a pilot study to enhance staff training and confidence when interacting with aggressive psychiatric patients using a virtual reality (VR) training module depicting an escalating patient scenario.

Significance: Dysregulated emotional outbursts, reactive aggression, and self-injurious behaviors are common in psychiatrically hospitalized patients. These behaviors result in aggressive patient interactions (APIs) which are associated with increased risk of harm to the patient and staff. Minimal research has examined interventions for successful training to effectively reduce or prevent API events and subsequent harm. Despite intensive, standardized trainings in crisis de-escalation protocols, staff continue to experience high rates of API injuries. More realistic training and competency in a safe environment to practice implementation and utilization of de-escalation strategies to avoid APIs and patient harm are needed.

Methods Using a pre – post, quasi-experimental design, 40 Behavioral Health Specialists and Registered Nurses at a pediatric psychiatric facility will participate in VR training depicting a commonly experienced scenario when interacting with an aggressive patient. Participants are stratified by job experience, sex, and VR experience. Study aims are to: i) assess the feasibility and usability of VR training among this population and ii) obtain measures of learner satisfaction and performance. Surveys measure usability, learner satisfaction, and coping with patient aggression. Pre- and post-performance in training will be compared and assessed by percent correct answers on the first attempt; time to correct answer; and the number of successful and unsuccessful attempts.

Preliminary Results (full analyses in progress): Preliminary survey results (N=14) show that 64% perceived the VR experience to be consistent with their real-world experiences: 87% agree that the VR training would help with interactions with aggressive patients: 71% reported the training was effective in identifying de-escalation strategies: 79% stated the training was effective in recognizing stages of patient crisis; training included important skills used in their job; and would recommend the training. Finally, 100% would participate in future VR trainings.

Anticipated Conclusions: We plan to show that using VR to supplement in-place training programs for high-risk situations can improve users’ understanding of essential de-escalation and crisis techniques. We anticipate results will show an enhanced ability and confidence when interacting with aggressive patients. Future studies will expand on results and examine implications on staff and patient harm. 

Check more information on the  VR-based Employee Safety Training. Therapeutic Crisis Intervention Simulation 

UC UPRISE

XR-Lab’s project was selected to join the Undergraduates Pursuing Research in Science and Engineering (UPRISE) Program
UNDERGRADUATE SUMMER RESEARCH PROGRAM IN SCIENCE AND ENGINEERING
May 6 – July 26, 2024

 

Project title: Reinforcement Learning (RL) system in Game Engine

Student: Mikhail Nikolaenko. UC. PI: Ming Tang. 

This project proposes the development of a sophisticated reinforcement learning (RL) system utilizing the robust and versatile environment of Unreal Engine 5 (UE5). The primary objective is to create a flexible and highly realistic simulation platform that can model a multitude of real-life scenarios, ranging from object recognition, urban navigation to emergency response strategies. This platform aims to significantly advance the capabilities of RL algorithms by exposing them to complex, diverse, and dynamically changing environments. Leveraging the advanced graphical and physical simulation capabilities of UE5, the project will focus on creating detailed and varied scenarios in which RL algorithms can be trained and tested. These scenarios will include, but not be limited to, urban traffic systems, natural disaster simulations, and public safety response models. The realism and intricacy of UE5’s environment will provide a challenging and rich training ground for RL models, allowing them to learn and adapt to unpredictable variables akin to those in the real world.

Synthetic material for AI training

Using a game engine to generate synthetic training data offers significant advantages for AI training in image classification, object detection, and animation classification. Synthetic data, created in controlled virtual environments, allows for generating large, diverse, and perfectly labeled datasets. This contrasts with human-labeled material, which is often expensive, time-consuming, and prone to errors. Synthetic data can be tailored to specific needs, ensuring comprehensive coverage of various scenarios and edge cases that might be underrepresented in real-world data. Additionally, synthetic environments facilitate the manipulation of variables, such as lighting and angles, providing a robust training ground for AI models. Overall, the use of synthetic material enhances the efficiency and accuracy of AI training compared to traditional human-labeled datasets.

Example

The Roof AI Training project focuses on training an AI system using Unreal Engine 5 (UE5) to generate synthetic data for machine learning applications, specifically for detecting damaged asphalt shingles on roofs. It highlights the need for massive labeled datasets to train AI effectively and discusses the role of Convolutional Neural Networks (CNNs) in image recognition tasks. UE5 is utilized to create procedurally generated roofing textures and environments, providing a cost-effective and scalable solution for data generation. The research project involves generating high-quality synthetic images of roofs, including damaged areas, to train and evaluate a CNN model. The AI aspect includes using Simplex Noise and 3-tab pattern algorithms for realistic texture generation, followed by training a CNN using PyTorch on the generated data. Preliminary results indicate that the model accurately detects most missing shingles, though further improvements are needed in bounding box precision and detection accuracy. Future goals include enhancing the procedural generation algorithm, optimizing the CNN architecture for better performance, and iteratively refining the AI through continuous training and evaluation using both synthetic and real-world data.

 

Related projectsBuilding Safety Analysis with Machine Learning

Honors Seminar student projects

“Human-Computer Interaction in the Age of Extended Reality & Metaverse” student projects

Spring. 2024.  UC

Under the guidance of Ming Tang, Director of the XR-Lab at Digital Futures and DAAP, UC, this honors seminar course has propelled students through an immersive journey into the realm of XR. The course encompasses Extended Reality, Metaverse, and Digital Twin technologies, providing a comprehensive platform for theoretical exploration and practical application in XR development.

The coursework showcases an array of student-led research projects that investigate the role of XR in various domains, including medical training, flight simulation, entertainment, tourism, cultural awareness, fitness, and music. Through these projects, students have had the opportunity to not only grasp the intricate theories underpinning future HCI developments but also to apply their skills in creating immersive experiences that hint at the future of human-technology interaction.

 

 “Human-Computer Interaction in the Age of Extended Reality & Metaverse” is a UC Honors course that delves into the burgeoning field of extended reality (XR) and its confluence with human-computer interaction (HCI), embodying a fusion of scholarly inquiry and innovative practice.

Ming Tang, Professor, Director of XR-Lab, DAAP, University of Cincinnati

Students: Nishanth Chidambaram, Bao Huynh, Caroline McCarthy, Cameron Moreland, Frank Mularcik, Cooper Pflaum, Triet Pham, Brooke Stephenson, Pranav Venkataraman

Thanks for the support from the UC Honors Program and UC Digital Futures.

Digital Twin of Cincinnati

A realtime flythrough demo for Digital Twin of City Cincinnati

Digital Futures Building at the University of Cincinnati

Destroy Alien buildings near the UC campus. Project developed by students Cooper Pflaum and Nishanth Chidambaram.