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ABSTRACT Medical procedures are an essential part of healthcare delivery, and the acquisition of procedural skills is a critical component of medical education. Unfortunately, procedural skill is not evenly distributed among medical providers. Skills may vary within departments or institutions, and across geographic regions, depending on the provider’s training and ongoing experience. We present a mixed reality real-time communication system to increase access to procedural skill training and to improve remote emergency assistance. Our system allows a remote expert to guide a local operator through a medical procedure. RGBD cameras capture a volumetric view of the local scene including the patient, the operator, and the medical equipment. The volumetric capture is augmented onto the remote expert’s view to allow the expert to spatially guide the local operator using visual and verbal instructions. We evaluated our mixed reality communication system in a study in which experts teach the ultrasound-guided placement of a central venous catheter (CVC) to students in a simulation setting. The study compares state-of-theart video communication against our system. The results indicate that our system enhances and offers new possibilities for visual communication compared to video teleconference-based training.
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Features of adaptive training algorithms for improved complex skill acquisition
Training complex skills is typically accomplished by means of a trainer or mediator who tailors instruction to the individual trainee. However, facilitated training is costly and labor intensive, and the use of a mediator is infeasible in remote or extreme environments. Imparting complex skills in applications like long-duration human spaceflight, military field operations, or remote medicine may require automated training algorithms. Virtual reality (VR) is an effective, easily programmable, immersive training medium that has been used widely across fields. However, there remain open questions in the search for the most effective algorithms for guiding automated training progression. This study investigates the effects of responsiveness, personalization, and subtask independence on the efficacy of automated training algorithms in VR for training complex, operationally relevant tasks. Thirty-two subjects (16M/16F, 18–54 years) were trained to pilot and land a spacecraft on Mars within a VR simulation using four different automated training algorithms. Performance was assessed in a physical cockpit mock-up. We found that personalization results in faster skill acquisition on average when compared with a standardized progression built for a median subject (p= 0.0050). The standardized progression may be preferable when consistent results are desired across all subjects. Independence of the difficulty adjustments between subtasks may lead to increased skill acquisition, while lockstep in the progression of each subtask increases self-reported flow experience (p= 0.01), fluency (p= 0.02), and absorption (p= 0.01) on the Flow Short Scale. Data visualization suggests that highly responsive algorithms may lead to faster learning progressions and higher skill acquisition for some subjects. Improving transfer of skills from training to testing may require either high responsiveness or a standardized training progression. Optimizing the design of automated, individually adaptive algorithms around the training needs of a group may be useful to increase skill acquisition for complex operational tasks.
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- Award ID(s):
- 1955109
- PAR ID:
- 10524557
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Virtual Reality
- Volume:
- 5
- ISSN:
- 2673-4192
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frvir.2024.1322656
