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Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Objective: This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving. Background: Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems. Method: Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions. Results: Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model. Conclusion: The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time. Application: The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving. [ABSTRACT FROM AUTHOR]

Worker and work-related musculoskeletal symptoms are prevalent among surgeons operating on human patients. Despite incidence rates for accidents among veterinarians and their staff being 2.9 times higher than that of general practitioners of human medicine, little is known about musculoskeletal symptoms among veterinary surgeons. In this study, 212 board-certified members of the American College of Veterinary Surgeons responded to a survey regarding various work-related activities and their experience with musculoskeletal symptoms in 10 different body regions. Across all body regions, reported pain increased from before to after a typical day of surgery (p <.01). Gender, weight, age, and years performing surgery were worker factors that were related to pain (p <.05), while number of procedures, practice focus, and proportion of minimally invasive surgery were work factors related to pain (p <.05). Our findings suggest that musculoskeletal symptoms are prevalent among veterinary surgeons and may help provide evidence for guidelines for minimising musculoskeletal injuries in veterinary surgery. Practitioner summary: Little is known about the risk factors for musculoskeletal symptoms (MSS) among veterinary surgeons. This cross-sectional survey of veterinary surgeons investigates worker and work factors related to MSS. We show that MSS are prevalent and identify key factors providing evidence that MSS are a concern in veterinary surgery. [ABSTRACT FROM AUTHOR]

Objective: This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS). Background: The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL. Method: The MWL-AA presented in this study was a semi-autonomous suction tool. The first experiment recruited ten participants to perform surgical tasks under different MWL levels. The physiological responses were captured and used to develop a real-time multi-sensing model for MWL detection. The second experiment evaluated the effectiveness of the MWL-AA, where nine brand-new surgical trainees performed the surgical task with and without the MWL-AA. Mixed effect models were used to compare task performance, objective- and subjective-measured MWL. Results: The proposed system predicted high MWL hemorrhage conditions with an accuracy of 77.9%. For the MWL-AA evaluation, the surgeons' gaze behaviors and brain activities suggested lower perceived MWL with MWL-AA than without. This was further supported by lower self-reported MWL and better task performance in the task condition with MWL-AA. Conclusion: A MWL-AA systems can reduce surgeons' workload and improve performance in a high-stress hemorrhaging scenario. Findings highlight the potential of utilizing MWL-AA to enhance the collaboration between the autonomous system and surgeons. Developing a robust and personalized MWL-AA is the first step that can be used do develop additional use cases in future studies. Application: The proposed framework can be expanded and applied to more complex environments to improve human-robot collaboration. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to identify objective measures that predict surgeon nontechnical skills (NTS) during surgery. Background: NTS are cognitive and social skills that impact operative performance and patient outcomes. Current methods for NTS assessment in surgery rely on observation-based tools to rate intraoperative behavior. These tools are resource intensive (e.g., time for observation or manual labeling) to perform; therefore, more efficient approaches are needed. Method: Thirty-four robotic-assisted surgeries were observed. Proximity sensors were placed on the surgical team and voice recorders were placed on the surgeon. Surgeon NTS was assessed by trained observers using the NonTechnical Skills for Surgeons (NOTSS) tool. NTS behavior metrics from the sensors included communication, speech, and proximity features. The metrics were used to develop mixed effect models to predict NOTSS score and in machine learning classifiers to distinguish between exemplar NTS scores (highest NOTSS score) and non-exemplar scores. Results: NTS metrics were collected from 16 nurses, 12 assistants, 11 anesthesiologists, and four surgeons. Nineteen behavior features and overall NOTSS score were significantly correlated (12 communication features, two speech features, five proximity features). The random forest classifier achieved the highest accuracy of 70% (80% F1 score) to predict exemplar NTS score. Conclusion: Sensor-based measures of communication, speech, and proximity can potentially predict NOTSS scores of surgeons during robotic-assisted surgery. These sensing-based approaches can be utilized for further reducing resource costs of NTS and team performance assessment in surgical environments. Application: Sensor-based assessment of operative teams' behaviors can lead to objective, real-time NTS measurement. [ABSTRACT FROM AUTHOR]

Objective: (1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU. Background: Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data. Methods: Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes. Results: While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter. Conclusion: Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development. [ABSTRACT FROM AUTHOR]

Objective: The goal of this systematic literature review is to investigate the relationship between indirect physiological measurements and direct measures of situation awareness (SA). Background: Across different environments and tasks, assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SAGAT, SPAM, and/or SART. However, research suggests that indirect physiological sensing methods may also be capable of predicting SA. Currently, it is unclear which particular physiological approaches are sensitive to changes in SA. Method: Seven databases were searched using the PRISMA reporting guidelines. Eligibility criteria included human-subject experiments that used at least one direct SA assessment technique, as well as at least one physiological measurement. Information extracted from each article was the physiological metric(s), the direct SA measurement(s), the correlation between these two metrics, and the experimental task(s). All studies underwent a quality assessment. Results: Twenty-five articles were included in this review. Eye tracking techniques were the most commonly used physiological measures, and correlations between conscious aspects of eye movement measures and direct SA scores were observed. Evidence for cardiovascular predictors of SA were mixed. EEG studies were too few to form strong conclusions, but were consistently positive. Conclusion: Further investigation is needed to methodically collect more relevant data and comprehensively model the relationships between a wider range of physiological measurements and direct assessments of SA. Application: This review will guide researchers and practitioners in methods to indirectly assess SA with sensors and highlight opportunities for future research on wearables and SA. [ABSTRACT FROM AUTHOR]