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Exoskeleton as a human augmentation technology has shown a great potential for transforming the future civil engineering operations. However, the inappropriate use of exoskeleton could cause injuries and damages if the user is not well trained. An effective procedural and operational training will make users more aware of the capabilities, restrictions and risks associated with exoskeleton in civil engineering operations. At present, the low availability and high cost of exoskeleton systems make hands-on training less feasible. In addition, different designs of exoskeleton correspond with different activation procedures, muscular engagement and motion boundaries, posing further challenges to exoskeleton training. We propose an “sensation transfer” approach that migrates the physical experience of wearing a real exoskeleton system to first-time users via a passive haptic system in an immersive virtual environment. The body motion and muscular engagement data of 15 experienced exoskeleton users were recorded and replayed in a virtual reality environment. Then a set of haptic devices on key parts of the body (shoulders, elbows, hands, and waist) generate different patterns of haptic cues depending on the trainees’ accuracy of mimicking the actions. The sensation transfer method will enhance the haptic learning experience and therefore accelerate the training. 

Background Stress affects learning during training, and virtual reality (VR) based training systems that manipulate stress can improve retention and retrieval performance for firefighters. Brain imaging using functional Near Infrared Spectroscopy (fNIRS) can facilitate development of VR-based adaptive training systems that can continuously assess the trainee’s states of learning and cognition. Objective The aim of this study was to model the neural dynamics associated with learning and retrieval under stress in a VR-based emergency response training exercise. Methods Forty firefighters underwent an emergency shutdown training in VR and were randomly assigned to either a control or a stress group. The stress group experienced stressors including smoke, fire, and explosions during the familiarization and training phase. Both groups underwent a stress memory retrieval and no-stress memory retrieval condition. Participant’s performance scores, fNIRS-based neural activity, and functional connectivity between the prefrontal cortex (PFC) and motor regions were obtained for the training and retrieval phases. Results The performance scores indicate that the rate of learning was slower in the stress group compared to the control group, but both groups performed similarly during each retrieval condition. Compared to the control group, the stress group exhibited suppressed PFC activation. However, they showed stronger connectivity within the PFC regions during the training and between PFC and motor regions during the retrieval phases. Discussion While stress impaired performance during training, adoption of stress-adaptive neural strategies (i.e., stronger brain connectivity) were associated with comparable performance between the stress and the control groups during the retrieval phase. 

Amid the rapid development of building information technologies, wayfinding information has become more accessible to building users and first responders. As a result, a realistic risk of cognitive load related to the wayfinding information processing starts to emerge. As cognition-driven adaptive wayfinding information systems become increasingly captivated to overcome challenges of cognition overload due to overwhelming information, a practical and non-invasive method to monitor and classify cognitive loads during the processing of wayfinding information is needed. This paper tests a Functional Near-Infrared Spectroscopy (fNIRS) based method to identify cognitive load related to wayfinding information processing. It provides a holistic fNIRS signal analytical pipeline to extract hemodynamic response features in the prefrontal cortex (PFC) for cognitive load classification. A human-subject experiment (N=15) based on the Sternberg working memory test was performed to model the relationship between fNIRS features and cognitive load. Personalized models were also evaluated to capture individual differences and identify unique contributing features to each person. The results find that fNIRS-based model can help classify cognitive load changes driven by the different levels of task difficulty with satisfactory performance (avg. accuracy rate 70.02±4.41 percent). The findings also demonstrate that personalized models, instead of universal models, are needed for classifying cognitive load based on neuroimaging data. fNIRS has demonstrated comparable advantages over other neuroimaging methods in cognitive load classification given its robustness to motion artifacts and the satisfactory predictability.