(D-158) Inertial Sensor for Improving Surgical Ergonomics

Surgeons are at a high risk for work-related musculoskeletal disorders (WRMDs) due to long hours of sustained sub-optimal posture, and prolonged exertion while operating [1]. The study of ergonomics analyzes how workers interact with their working environments and how this interaction influences task-related performance [2]. Otolaryngology is a surgical field with high ergonomic risk due to repetitive fine motor movements in unfavorable postures [2]. A study on Otolaryngology and Head and Neck surgery in Canada, reported that 97% of respondents experienced some physical symptom in one or many regions of the body. Additionally, 74% noted an exacerbation of their musculoskeletal symptoms by work [3]. These symptoms are attributed to factors such as loupes, head-mounted lighting, endoscopes, microscopes, and operating without a monitor in endoscopic surgeries [1]. Another study found that neck pain was most common (59.7%) followed by back pain (56.5%) and hands/wrists pain (19.4%) [4]. Furthermore, studies have shown that Otolaryngologists and Head and Neck surgeons have poor work posture during more than 80% of operating time and spend 54% of their operating time with the head bent forward, to one side, or twisted [3]. Due to WRMD, there is a desire from surgeons to learn more about proper ergonomics and how they can be implemented in the operating room. However, there is a need for more data and aids to be developed. Therefore, the purpose of this study was to determine a way to record head position while performing surgery to gather useful data for further ergonomic considerations.   

The Xsens DOT motion sensor was purchased from Movella along with the sensor holder. The sensor was attached to an elastic headband using industrial strength Velcro straps. Additionally, a code was developed in Python to produce an output of the position data as well as a graph of the amount of time spent in a particular range of angles. Two main tests were done for verification of this device. For the first test, the accuracy of the Xsens DOT with the Python code was compared to a digital goniometer. This test was done by attaching the device to a purpose-built analog model of the head and trunk. Then, the model was placed in different positions in flexion and extension. To verify the code recorded the position data accurately, a digital goniometer was used to measure the angle from the neutral position. Additionally, we placed the device in incorrect positions on the head to determine how it would affect the accuracy of the device. Another verification test performed was comparing the measurements between the Xsens DOT and the Opal Sensors. The OPAL sensors were obtained from APDM Wearable Technologies. They are a set of IMU sensors that came with premade kinematic software that could measure different joint angles. For this test, a user put on the Xsens device and the Opal Sensors simultaneously. The flexion and extension angles recorded and the graphical output from both devices were compared.