Surgeons are at high risk of developing musculoskeletal disorders. This study was designed to identify risk factors and assess intraoperative physical stressors using subjective and objective measures, including type of procedure and equipment used. Wearable sensors and pre- and postoperation surveys were analyzed. Data from 116 cases (34 male and 19 female surgeons) were collected across surgical specialties. Surgeons reported increased pain in the neck, upper, and lower back both during and after operations. High-stress intraoperative postures were also revealed by the real-time measurement in the neck and back. Surgical duration also impacted physical pain and fatigue. Open procedures had more stressful physical postures than laparoscopic procedures. Loupe usage negatively impacted neck postures. This study highlights the fact that musculoskeletal disorders are common in surgeons and characterizes surgeons' intraoperative posture as well as surgeon pain and fatigue across specialties. Defining intraoperative ergonomic risk factors is of paramount importance to protect the well-being of the surgical workforce.

There is a need for objective methods for upper arm elevation measurements for accurate and convenient risk assessments. The aims of this study were (i) to compare a newly developed iOS application (iOS) for measuring upper arm elevation and angular velocity with a reference optical tracking system (OTS), and (ii) to compare the accuracy of the iOS incorporating a gyroscope and an accelerometer with using only an accelerometer, which is standard for inclinometry. The iOS-OTS limits of agreement for static postures (9 subjects) were -4.6° and 4.8°. All root mean square differences in arm swings and two simulated work tasks were 0.98. The mean absolute iOS-OTS difference of median angular velocity was <13.1°/s, which was significantly lower than only using an accelerometer (<43.5°/s). The accuracy of this iOS application compares well to that of today's research methods and it can be useful for practical upper arm measurements.

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R² = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R² = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R² = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.

Work-related musculoskeletal and cardiovascular disorders are still prevalent in today's working population. Nowadays, risk assessments are usually performed via self-reports or observations, which have relatively low reliability. Technology developments in textile electrodes (textrodes), inertial measurement units, and the communication and processing capabilities of smart phones/tablets provide wearable solutions that enable continuous measurements of physiological and musculoskeletal loads at work with sufficient reliability and resource efficiency. In this paper, a wearable system integrating textrodes, motion sensors, and real-time data processing through a mobile application was developed as a demonstrator of risk assessment related to different types and levels of workload and activities. The system was demonstrated in eight subjects from four occupations with various workload intensities, during which the heart rate and leg motion data were collected and analyzed with real-time risk assessment and feedback. The system showed good functionality and usability as a risk assessment tool. The results contribute to designing and developing future wearable systems and bring new solutions for the prevention of work-related disorders.