Granular estimation of user cognitive workload using multi-modal physiological sensors.

Mental workload (MWL) is a crucial area of study due to its significant influence on task performance and potential for significant operator error. However, measuring MWL presents challenges, as it is a multi-dimensional construct. Previous research on MWL models has focused on differentiating between two to three levels. Nonetheless, tasks can vary widely in their complexity, and little is known about how subtle variations in task difficulty influence workload indicators. To address this, we conducted an experiment inducing MWL in up to 5 levels, hypothesizing that our multi-modal metrics would be able to distinguish between each MWL stage. We measured the induced workload using task performance, subjective assessment, and physiological metrics. Our simulated task was designed to induce diverse MWL degrees, including five different math and three different verbal tiers. Our findings indicate that all investigated metrics successfully differentiated between various MWL levels induced by different tiers of math problems. Notably, performance metrics emerged as the most effective assessment, being the only metric capable of distinguishing all the levels. Some limitations were observed in the granularity of subjective and physiological metrics. Specifically, the subjective overall mental workload couldn't distinguish lower levels of workload, while all physiological metrics could detect a shift from lower to higher levels, but did not distinguish between workload tiers at the higher or lower ends of the scale (e.g., between the easy and the easy-medium tiers). Despite these limitations, each pair of levels was effectively differentiated by one or more metrics. This suggests a promising avenue for future research, exploring the integration or combination of multiple metrics. The findings suggest that subtle differences in workload levels may be distinguishable using combinations of subjective and physiological metrics.

Adaptive Human-Robotic Interaction for Robotic-assisted Surgical Settings.

INTRODUCTION: Increased complexity in robotic-assisted surgical system interfaces introduces problems with human-robot collaboration that result in excessive mental workload (MWL), adversely impacting a surgeon's task performance and increasing error probability. Real-time monitoring of the operator's MWL will aid in identifying when and how interventions can be best provided to moderate MWL. In this study, an MWL-based adaptive automation system is constructed and evaluated for its effectiveness during robotic-assisted surgery. MATERIALS AND METHODS: This study recruited 10 participants first to perform surgical tasks under different cognitive workload levels. Physiological signals were obtained and employed to build a real-time system for cognitive workload monitoring. To evaluate the effectiveness of the proposed system, 15 participants were recruited to perform the surgical task with and without the proposed system. The participants' task performance and perceived workload were collected and compared. RESULTS: The proposed neural network model achieved an accuracy of 77.9% in cognitive workload classification. In addition, better task performance and lower perceived workload were observed when participants completed the experimental task under the task condition supplemented with adaptive aiding using the proposed system. CONCLUSIONS: The proposed MWL monitoring system successfully diminished the perceived workload of participants and increased their task performance under high-stress conditions via interventions by a semi-autonomous suction tool. The preliminary results from the comparative study show the potential impact of automated adaptive aiding systems in enhancing surgical task performance via cognitive workload-triggered interventions in robotic-assisted surgery.

Worker and work-related factors influence on musculoskeletal symptoms among veterinary surgeons.

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.

An Eye-Fixation Related Electroencephalography Technique for Predicting Situation Awareness: Implications for Driver State Monitoring Systems.

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.

Development of an Automated Composite Ureteroscopic Efficiency Score Through Simulated Ureteroscopic Skills Assessment.

Introduction: Flexible ureteroscopy (fURS) is the most common procedure for treatment of urolithiasis. We previously utilized kinematic evaluations of simulated fURS to demonstrate that certain body movements are associated with efficient ureteroscopic manipulation for complex tasks. In this study, we incorporated computer vision to create an efficiency score using the ureteroscope travel distance (DIST), task time (TIME), spectral arc length (SPARC), and percentage of purposeful wall collisions (COLL). The goal is a simulation-based system that can abstract these automated performance metrics (APMs) to differentiate between novice and expert ureteroscope handling. Methods: A ureteroscopic simulation box was used. Body kinematics, task time, and ureteroscopic movements were analyzed using a motion capture system and video camera. Optical flow computer vision was used to track the ureteroscope. DIST, TIME, and SPARC were automatically calculated. Wall collisions were automatically captured and independently judged by two authors; an algorithm was developed to automatically determine the COLL variable. A mixed-effects model was used to aggregate these variables and distinguish between surgeons' first and final task attempts. Normalized values of these metrics were added to create a composite ureteroscopic efficiency score (CUES). Results: Twelve urologists completed the simulated tasks. The COLL assessment algorithm determined beneficial wall collisions with an accuracy of 77%. Normalized values of TIME, DIST, SPARC, and COLL were combined to create a composite ureteroscopic efficiency score (CUES). Compared with the first attempt, both the second and third attempts showed statistically significant improvements in CUES. The ROC-AUC score reached 0.86, suggesting excellent discrimination between attempts. There was also a statistically significant difference in CUES when comparing resident and attending performance. Conclusions: APMs can be abstracted using computer vision and artificial intelligence; an aggregate composite score (CUES) may be a promising method for evaluation of ureteroscopic efficiency.

A sensor-based framework for layout and workflow assessment in operating rooms.

Due to their large sizes and impediments to personnel workflows, integrating robotic technologies into the existing operating rooms (OR) is a challenge. In this study, we developed an ultra-wideband sensor-based human-machine-environment framework for layout and workflow assessments within the OR. In addition to providing best practices for use of the framework, we also demonstrated its effectiveness in understanding layout and workflow inefficiencies in 12 robotic-assisted surgeries (RAS) across 4 different surgical specialties. We found avoidable movements as the circulating nurse covers at least twice the distance of any other OR personnel before the patient cart (robot) is docked. OR areas of congestion and undesirable personnel-pair proximities across RAS phases that impose extra non-technical skill challenges were determined. Our findings highlight several implications for the added complexity of integrating robotic technologies into the OR, which can serve as drivers for objective evidence-based recommendations to combat RAS OR layout and workflow inefficiencies.

Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

Detailing experienced nurse decision making during acute patient care simulations.

INTRODUCTION: Nurse decision making (DM) is critical for patient safety. Eye-tracking methods can effectively assess nurse DM. The purpose of this pilot study was to use eye-tracking methods to assess nurse DM during a clinical simulation. MATERIALS AND METHODS: Experienced nurses managed a simulated patient manikin who suffered from a stroke mid-simulation. We assessed nurses' gaze patterns prior to and after the stroke. DM in general was assessed by nursing faculty using a clinical judgement rubric, and dichotomously based on recognition of the stroke or not. RESULTS: Data from eight experienced nurses was examined. For the nurses who recognized the stroke, visual attention was focused on the vital sign monitor and patient's head, which suggest those locations were consistently examined for correct decision-makers. CONCLUSIONS: Dwell time on general AOIs was associated with poorer DM, which may reflect poorer pattern recognition. Eye-tracking metrics may be effective to objectively assess nurse DM.

Prevalence of Work-Related Musculoskeletal Symptoms in Veterinary Surgeons-A Cross-Sectional Survey.

OBJECTIVES: The aim of this study was to determine the prevalence of work-related musculoskeletal symptoms (MSS) in veterinary surgeons using an online survey. STUDY DESIGN: An online survey was distributed to 1,031 diplomates of American College of Veterinary Surgeons. Responses were collected with data regarding surgical activities, experience with various types of MSS in 10 different body sites and attempts to reduce MSS. RESULTS: Two hundred and twelve respondents (21% response rate) completed the distributed survey in 2021. Ninety-three per cent of respondents had experienced MSS associated with surgery in at least one body part, with the neck, lower back and upper back frequently affected. Musculoskeletal discomfort and pain worsened with prolonged surgical hours. Forty-two per cent of them suffered from chronic pain persisting longer than 24 hours after surgeries. Musculoskeletal discomfort was common regardless of practice emphasis and procedure types. Forty-nine per cent of respondents with musculoskeletal pain had taken medication, 34% sought physical therapy for MSS and 38% ignored the symptoms. Over 85% of respondents showed more than some concern regarding career longevity due to musculoskeletal pain. CLINICAL SIGNIFICANCE: Work-related MSS are common in veterinary surgeons, and the results of this study warrant longitudinal clinical studies to determine risk factors and attention to workplace ergonomics in veterinary surgery.

Investigating gripping force during lifting tasks using a pressure sensing glove system.

Lifting tasks remain one of the leading causes of musculoskeletal disorders (MSDs), primarily in the low back region. Lifting analysis tools are, therefore, designed for assessing the risk of low back pain. Shoulder musculoskeletal problems have emerged as common MSDs associated with manual handling tasks. It is hypothesized that gripping force is related to lifting conditions and may be used as a supplementary risk metric for MSDs in the shoulder and low back regions, because it measures additional hand exertions for coupling the lifted object during lifting. We assessed the capability tactile gloves for measuring the gripping force during lifting as a means for assessing different task conditions (lifting weight, lifting height, lifting direction, body rotation, and handle). Thirty participants wore the tactile gloves and performed simulated lifting tasks. Regression models were used to analyze the effects of the task variables on estimating the measured gripping force. Results demonstrated that 58% and 70% of the lifting weight variance were explained by the measured gripping force without and with considering the individual difference, respectively. In addition to the lifting risk measures commonly used by practitioners, this study suggests a potential for using gripping force as a supplementary or additional risk metric for MSDs.

Physiological Measurements of Situation Awareness: A Systematic Review.

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.

Evaluating the Ergonomics of Surgical Residents During Laparoscopic Simulation: A Novel Computerized Approach.

BACKGROUND: Assessment of residents' body positioning during laparoscopy has not been adequately investigated. This study presents a novel computer vision technique to automate ergonomic evaluation and demonstrates this approach through simulated laparoscopy. METHODS: Surgical residents at a single academic institution were video recorded performing tasks from the Fundamentals of Laparoscopic Surgery (FLS). Ergonomics were assessed by 2 raters using the Rapid Upper Limb Assessment (RULA) tool. Additionally, a novel computer software program was used to measure ergonomics from the video recordings. All participants completed a survey on musculoskeletal complaints, which was graded by severity. RESULTS: Ten residents participated; all performed FLS in postures that exceeded acceptable ergonomic risks as determined by both the human and computerized RULA scores (P < .001). Lower-level residents scored worse than upper-level residents on the human-graded RULA assessment (P = .04). There was no difference in computer-graded RULA scores by resident level (P = .39) and computer-graded scores did not correlate with human scores (P = .75). Shoulder and wrist position were the greatest contributors to higher computer-graded scores (P < .001). Self-reported musculoskeletal complaints did not differ at resident level (P = .74); however, all residents reported having at least 1 form of musculoskeletal complaint occurring "often." CONCLUSIONS: Surgery residents demonstrated suboptimal ergonomics while performing simulated laparoscopic tasks. A novel computer program to measure ergonomics did not agree with the scores generated by the human raters, although it concluded that resident ergonomics remain a concern, especially regarding shoulder and wrist positioning.

Utilizing Eye Tracking to Assess Medical Student Non-Technical Performance During Scenario-Based Simulation: Results of a Pilot Study.

Background: Non-technical skills (NTS) are essential for safe surgical patient management. However, assessing NTS involves observer-based ratings, which can introduce bias. Eye tracking (ET) has been proposed as an effective method to capture NTS. The purpose of the current study was to determine if ET metrics are associated with NTS performance. Methods: Participants wore a mobile ET system and participated in two patient care simulations, where they managed a deteriorating patient. The scenarios featured several challenges to leadership, which were evaluated using a 4-point Likert scale. NTS were evaluated by trained raters using the Non-Technical Skills for Surgeons (NOTSS) scale. ET metrics included percentage of fixations and visits on areas of interest. Results: Ten medical students participated. Average visit duration on the patient was negatively correlated with participants' communication and leadership. Average visit duration on the patient's intravenous access was negatively correlated with participants' decision making and situation awareness. Conclusions: Our preliminary data suggests that visual attention on the patient was negatively associated with NTS and may indicate poor comprehension of the patient's status due to heightened cognitive load. In future work, researchers and educators should consider using ET to objectively evaluate and provide feedback on their NTS.

An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool.

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.

Characterizing Exposure to Physical Risk Factors during Veterinary Surgery with Wearable Sensors: A Pilot Study.

OCCUPATIONAL APPLICATIONSVeterinarians provide comprehensive health services for animals, but despite exposure to similar occupational and safety hazards as medical physicians, physical risk factors for these doctors and healthcare teams have not been characterized. In this pilot study, we used wearable sensor technology and showed that veterinary surgeons commonly experience static and demanding postures while performing soft tissue and orthopedic surgeries. Observations showed that muscle activation was highest in the right trapezius. Job factors such as surgical role (attending vs. assisting) and surgical specialty (soft tissue vs. orthopedics) appeared to influence exposure to physical risk factors. These findings suggest a need to consider the unique demands of surgical specialties in order to address the key risk factors impacting injury risks among veterinarians. For example, static postures may be a priority for soft tissue surgeons, while tools that reduce force requirements are more pressing for orthopedic surgeons.

BACKGROUND: Although musculoskeletal fatigue, pain, and injuries are commonly reported among surgeons in veterinary medicine, few studies have objectively characterized the exposure to physical risk factors among veterinary surgeons. Purpose: This study aimed to characterize muscle activation and postures of the neck and shoulders during live veterinary surgeries in the soft tissue and orthopedic specialties. Methods: Forty-four ergonomic exposure assessments (exposures) were collected during 26 surgical procedures across five surgeons. Exposures were collected from both soft tissue (n = 23) and orthopedic (n = 21) specialties. Physical risk factors were characterized by: (1) directly measuring muscle activation and posture of the neck and shoulders, using surface electromyography and inertial measurement units, respectively; and (2) collecting self-reported workload, pain, and stiffness. Results: Across the 44 exposures, neck and back symptoms respectively worsened after the surgery in 27% and 14% of the exposures. Veterinary surgeons exhibited neck postures involving a mean of 17° flexion during the surgical procedures. Static postures were common, occurring during 53­80% of the procedures. Compared to soft tissue procedures (e.g., 13.2% MVC in the right trapezius), higher muscle activity was observed during orthopedic procedures (e.g., 27.6% MVC in the right trapezius). Conclusions: This pilot study showed that physical risk factors (i.e., muscle activity and posture of the neck/shoulder) can be measured using wearable sensors during live veterinary surgeries. The observed risk factors were similar to those documented for medical physicians. Further studies are needed to bring awareness to opportunities for improving workplace ergonomics in veterinary medicine and surgery.

Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms.

OBJECTIVE: To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications. MATERIALS AND METHODS: We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and ML algorithms were compared. RESULTS: The propofol data had 3,300 infusion alerts, 92% of which were generated during the day shift and seven of which had a times-limit greater than 10. The mr-chart identified 15 alert pattern anomalies. Different thresholds were set to include the top 15 anomalies from each ML algorithm. A total of 31 unique ML anomalies were grouped and ranked by agreeability. All algorithms agreed on 10% of the anomalies, and at least two algorithms agreed on 36%. Each algorithm detected one specific anomaly that the mr-chart did not detect. The anomaly represented a day with 71 propofol alerts (half of which were overridden) generated at an average rate of 1.06 per infusion, whereas the moving alert rate for the week was 0.35 per infusion. DISCUSSION: These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. However, we recommend using a combination of algorithms, as multiple algorithms serve a benchmarking function and allow researchers to focus on data points with the highest algorithm agreeability. CONCLUSION: Unsupervised ML algorithms can assist clinicians in identifying unusual alert patterns as a first step toward achieving safer infusion practices.

Objective Nontechnical Skills Measurement Using Sensor-based Behavior Metrics in Surgical Teams.

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.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking.

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.

A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery.

Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon's dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant's tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.

A comparison of laparoscopic and robotic ergonomic risk.

INTRODUCTION: Work related injuries in minimally invasive surgery (MIS) are common because of the strains placed on the surgeon's or assistant's body. The objective of this study was to compare specific ergonomic risks among surgeons and surgical trainees performing robotic and laparoscopic procedures. MATERIALS AND METHODS: Ergonomic data and discomfort questionnaires were recorded from surgeons and trainees (fellows/residents) for both robotic and laparoscopic procedures. Perceived discomfort questionnaires were recorded pre/postoperatively. Intraoperatively, biomechanical loads were captured using motion tracking sensors and electromyography (EMG) sensors. Perceived discomfort, body position and muscle activity were compared between robotic and laparoscopic procedures using a linear regression model. RESULTS: Twenty surgeons and surgical trainees performed 29 robotic and 48 laparoscopic procedures. Postoperatively, increases in right finger numbness and right shoulder stiffness and surgeon irritability were noted after laparoscopy and increased back stiffness after robotic surgery. Further, the laparoscopic group saw increases in right hand/shoulder pain (OR 0.8; p = 0.032) and left hand/shoulder pain (0.22; p < 0.001) compared to robotic. Right deltoid and trapezius excessive muscle activity were significantly higher in laparoscopic operations compared to robotic. Demanding and static positioning was similar between the two groups except there was significantly more static neck position required for robotic operations. CONCLUSION: Robotic assisted surgeries led to lower postoperative discomfort and muscle strain in both upper extremities, particularly dominant side of the surgeon, but increased static neck positioning with subjective back stiffness compared with laparoscopy. These recognized ergonomic differences between the two platforms can be used to raise surgeon awareness of their intraoperative posture and to develop targeted physical and occupational therapy interventions to decrease surgeon WMSDs and increase surgeon longevity.