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Storytelling has been established as a proven method to effectively communicate and assist in knowledge transfer. In recent years, there has been growing interest in improving the training and learning domain by using advanced technology such as Virtual Reality (VR). However, a gap exists between storytelling and VR, and it is as yet unclear how they can be combined to form an effective system that not only maintains the level of engagement and immersion provided by VR technology but also provides the core strengths of storytelling. In this paper, we present vIS, a Vocational Immersive Storytelling system, which bridges the gap between storytelling and VR. vIS focuses on vocational training, in which users are trained on how to use a mechanical micrometer by employing a creative fictional story embedded inside a virtual manufacturing plant’s workplace. For the evaluation, a two-phase user study with 30 participants was conducted to measure the system’s effectiveness and improvements in long-term training, as well as to examine user experience against traditional methods of training—2D videos and textual manuals. The results indicate that the user’s ability to retain their training after seven days was nearly equal for vIS and the 2D video-based technique and was considerably higher than the text-based technique.
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Enhancing lifeguard training through virtual reality
According to the CDC, over three thousand people die every year from drowning in the United States. Many of these fatalities are preventable with properly trained lifeguards. Traditional lifeguard training relies on videos and mock rescues. While these methods are important, they have their shortcomings. Videos are static and do not build muscle memory. Mock rescues are labor-intensive and potentially put others in danger. Virtual reality (VR) can be used as an alternative training tool, building muscle memory in a fully controlled and safe environment. With full control over variables such as weather, population, and other distractions, lifeguards can be better equipped to respond to any situation. The single most important aspect of life guarding is finding the victim. This head rotation skill can be practiced and perfected in VR before guards ever get onto the stand. It also allows guards to practice in uncommon but nevertheless dangerous conditions such as fog and large crowds. VR also allows the user to get immediate feedback about performance and where they can improve.
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- Award ID(s):
- 1757900
- PAR ID:
- 10146784
- Date Published:
- Journal Name:
- Electronic Imaging
- ISSN:
- 2470-1173
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Li-Jessen, Nicole Yee-Key (Ed.)The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model’s prediction accuracy on the Earable device’s classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2352/ISSN.2470-1173.2020.13.ERVR-339
