In 2007, total health-care spending in the United States reached US $2.3 trillion, and continues to rise at the fastest rate in US history. The design and implementation of better planning and scheduling systems is an important area to study in order to reduce high costs and improve access to services in the health-care system. Surgery scheduling, in particular, is an area with significant potential for realizing greater efficiencies. Poor scheduling prevents health-care providers from matching patient demand with available capacity, causing inefficient use of resources, decreased return on investment, and long waiting lists for patients. It has been estimated that surgery accounts for more than 40% of a hospital's total revenues and expenses. Recent studies indicate that resource utilization, overtime, and on-time start performance within surgical suites could be improved at most hospitals. These important performance measures are influenced in part by the surgery scheduling systems and policies that are used in practice.Surgery scheduling systems impact a variety of expensive resources including operating rooms (ORs), the postanesthesia care unit (PACU), intensive care unit (ICU), hospital beds, equipment resources such as mobile diagnostic imaging devices, and human resources including surgeons, nurses, anesthesiologists, and other staff. The unpredictable nature of surgery results in uncertainty in the duration of surgery and patient recovery. This can be caused by many factors including the varying experience of surgeons and OR teams, the presence of residents or surgical fellows in
As autonomous systems become more prevalent and their inner workings become more opaque, we increasingly rely on trust to guide our interactions with them especially in complex or rapidly evolving situations. When our expectations of what automation is capable of do not match reality, the consequences can be sub-optimal to say the least. The degree to which our trust reflects actual capability is known as trust calibration. One of the approaches to studying this is neuroergonomics. By understanding the neural mechanisms involved in human-machine trust, we can design systems which promote trust calibration and possibly measure trust in real time. Our study used the Multi Attribute Task Battery to investigate neural correlates of trust in automation. We used EEG to record brain activity of participants as they watched four algorithms of varying reliability perform the SYSMON subtask on the MATB. Subjects reported their subjective trust level after each round. We subsequently conducted an effective connectivity analysis and identified the cingulate cortex as a node, and its asymmetry ratio and incoming information flow as possible indices of trust calibration. We hope our study will inform future work involving decision-making and real-time cognitive state detection.
While sexual harassments are inappropriate behaviors in the society, the interpretation of and sensitivity toward sexual harassment can vary by individual. Differences across individuals, such as gender, may influence whether one interprets an action to be sexually harassing or not. Virtual reality technology enables human behavior assessment without interfacing with physical danger. The present work examined whether gender and body-gender transfer in VR influenced the perception of sexually harassing behaviors, and explored the utility of emerging technology in increasing one’s awareness of behaviors that may be considered sexually harassing. Participants (n=12) embodied in virtual characters of different genders and experienced seven sexually harassing scenarios in an immersive virtual environment in random order. In general, participants provided higher rating to the sensitivity toward sexual harassment in the VR harassment scenarios than those scenarios described on paper. There was an increase in participants’ sensitivity toward sexual harassment after experiencing sexual harassment scenarios from the perspective of the victim in VR. Participants perceived higher level of sexual harassment when they embodied in female avatars, which suggested there was an effect of VR with body-gender transfer on perception of sexual harassment. There were gender differences in awareness of harassing behaviors in VR environment, and VR may be a training method to narrow gender gap and increase awareness toward sexual harassment.
Excessive low back joint loading during material handling tasks is considered a critical risk factor of musculoskeletal disorders (MSD). Therefore, it is necessary to understand the low-back joint loading during manual material handling to prevent low-back injuries. Recently, computer vision-based pose reconstruction methods have shown the potential in human kinematics and kinetics analysis. This study performed L5/S1 joint moment estimation by combining VideoPose3D, an open-source pose reconstruction library, and a biomechanical model. Twelve participants lifting a 10 kg plastic crate from the floor to a knuckle-height shelf were captured by a camera and a laboratory-based motion tracking system. The L5/S1 joint moments obtained from the camera video were compared with those obtained from the motion tracking system. The comparison results indicate that estimated total peak L5/S1 moments during lifting tasks were positively correlated to the reference L5/S1 joint moment, and the percentage error is 7.7%.
Human-robot collaboration is a flourishing work configuration in modern plants. Yet, the potentially hazardous collision between human workers and collaborative robots raises safety concerns. In this study, we proposed a collision avoidance method in which a single camera and a computer-vision algorithm were deployed to sense the location of human workers. Two collision avoidance schemes were further developed to determine the timing for robot to retract its arm. Specifically, the static scheme continuously monitors whether a worker is in a hazard zone, while the dynamic scheme predicts worker’s position after a short time, and monitors whether the predicted worker’s position is in a hazard zone. Preliminary validation showed that our proposed method can effectively enable a collaborative robot to retract its arms when a worker is approaching.
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