Anthropometry is known as the science that studies the human body dimensions, this measurements are acquire using special devices and techniques whose results are analyzed through statistics. Anthropometry plays an important role within the industrial design process in areas such as clothing, ergonomics, and biomechanics, where statistical data about body medians allow optimizing product design. Recently, image processing and hardware advances are allowing the development of applications that allow an user to preview wardrobe, costumes, games or advergames and even different types of environments according to the user measurements. This project proposes the development of a complimentary tool for acquiring user anthropometric data for characterizing users in the Mil. Nueva Granada University in Colombia, South America using Microsoft's Kinect skeletal tracking for developing and assess the design of workspaces in several areas such as laboratories.

Here we present the anesthesia crisis scenario builder (ACSB) framework \red{prototype} that allows for the development of (or modification of existing), anesthesia crisis resource management-based virtual simulations. The ACSB framework was developed specifically to allow medical educators, who may have a limited (if any) programming/software development background, \red{to replicate and edit anesthesia scenarios based on pre-programmed steps from the Anaesthetic Crisis Manual (ACM).} The scenarios can be developed simply and intuitively using a graphical user interface. Results of a preliminary study examining the usability of the ACSB framework indicate the ease of creating a crisis scenario with limited, if any, programming knowledge.

Touch plays a fundamental role in our daily interactions, allowing us to interact with and perceive objects and their spatial properties. Despite its importance in the real-world, touch is often ignored in virtual environments. However, accurately simulating the sense of touch is difficult, requiring the use of high-fidelity haptic devices that are cost-prohibitive. Lower fidelity consumer-level haptic devices are becoming more widespread, yet are generally limited in perceived fidelity and the range of motion (degrees of freedom) required to realistically simulate many tasks. Studies into sound and vision suggest that the presence or absence of sound can influence task performance. Here, we explore whether the presence or absence of contextually relevant sound cues influences the performance of a simple haptic drilling task. Although the results of this study do not show any statistically significant difference in task performance with general (task-irrelevant) sound, we discuss how this is a necessary step in understanding the role of sound on haptic perception.

Geocaching has emerged as an activity to promote the discovery, and group work (competitive or collaborative), with physical activity that challenges participants around finding clues to solve puzzles using mobile devices. Geocaching has been in used in entertainment, teaching, and physical activity applications. Traditional approaches use QR codes, beacons, apps and websites to guide the user through the experience. However, current advances in mobile devices provide hardware and software features to create more immersive and interactive scenarios. In our project, we used augmented reality to provide engaging interactions and information about historic landmarks within the Mil. Nueva Granada University. The goal is to have fresh year students getting familiar with the landmarks and develop awareness of its significance. Each landmark triggers a mini-game that leads to next clue. To gather the student's thoughts on the app, participants were invited to try the app and fill a questionnaire.

We have recently developed the Unity Simulator for Epidural Insertion Training (USEIT) system that provides an innovative and relatively inexpensive virtual simulation approach for epidural training. This report describes the design and development process to produce the USEIT system.

Virtual reality (VR) enables the development of virtual training frameworks suitable for various domains, especially when real-world conditions may be hazardous or impossible to replicate because of unique additional resources (e.g., equipment, infrastructure, people, locations). Although VR technology has significantly advanced in recent years, methods for evaluating immersion (i.e., the extent to which the user is engaged with the sensory information from the virtual environment or is invested in the intended task) continue to rely on self-reported questionnaires, which are often administered after using the virtual scenario. Having an objective method to measure immersion is particularly important when using VR for training, education, and applications that promote the development, fine-tuning, or maintenance of skills. The level of immersion may impact performance and the translation of knowledge and skills to the real-world. This is particularly important in tasks where motor skills are combined with complex decision making, such as surgical procedures. Efforts to better measure immersion have included the use of physiological measurements including heart rate and skin response, but so far they do not offer robust metrics that provide the sensitivity to discriminate different states (idle, easy, and hard), which is critical when using VR for training to determine how successful the training is in engaging the user’s senses and challenging their cognitive capabilities. In this study, electroencephalography (EEG) data were collected from 14 participants who completed VR jigsaw puzzles with two different levels of task difficulty. Machine learning was able to accurately classify the EEG data collected during three different states, obtaining accuracy rates of 86% and 97% for differentiating easy versus hard difficulty states and baseline vs. VR states. Building on these results may enable the identification of robust biomarkers of immersion in VR, enabling real-time recognition of the level of immersion that can be used to design more effective and translative VR-based training. This method has the potential to adjust aspects of VR related to task difficulty to ensure that participants are immersed in VR.