School of Industrial Engineering, Purdue University, West Lafayette, IN, USA.
Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Ergonomics. 2021 Sep;64(9):1160-1173. doi: 10.1080/00140139.2021.1909753. Epub 2021 May 11.
Surface electromyography (sEMG) can monitor muscle activity and potentially predict fatigue in the workplace. However, objectively measuring fatigue is challenging in complex work with unpredictable work cycles where sEMG may be influenced by the dynamically changing posture demands. This study proposes a multi-modal approach integrating sEMG with motion sensors and demonstrates the approach in the live surgical work environment. Seventy-two exposures from twelve participants were collected, including self-reported musculoskeletal discomfort, sEMG, and postures. Posture sensors were used to identify time windows where the surgeon was static and in non-demanding positions, and mean power frequencies (MPF) were then calculated during those time windows. In 57 out of 72 exposures (80%), participants experienced an increase in musculoskeletal discomfort. Integrated (multi-modality) measurements showed better performance than single-modality (sEMG) measurements in detecting decreases in MPF, a predictor of fatigue. Based on self-reported musculoskeletal discomfort, sensor-based thresholds for identifying fatigue are proposed for the trapezius and deltoid muscle groups. Work-related fatigue is one of the intermediate risk factors to musculoskeletal disorders. This article presents an objective integrated approach to identify musculoskeletal fatigue using wearable sensors. The presented approach could be implemented by ergonomists to identify musculoskeletal fatigue more accurately and in a variety of workplaces. sEMG: surface electromyography; IMU: inertia measurement unit; MPF: mean power frequency; ACGIH: American Conference of Governmental Industrial Hygienists; SAGES: Society of American Gastrointestinal and Endoscopic Surgeons; LD: left deltoid; LT: left trapezius; RD: right deltoid; RT: right trapezius.
表面肌电图（sEMG）可监测肌肉活动，并有可能预测工作场所的疲劳。然而，在具有不可预测工作周期的复杂工作中，客观测量疲劳是具有挑战性的，在这种工作中，sEMG 可能会受到动态变化的姿势需求的影响。本研究提出了一种将 sEMG 与运动传感器相结合的多模态方法，并在现场手术工作环境中展示了该方法。从 12 名参与者中收集了 72 个暴露情况，包括肌肉骨骼不适的自我报告、sEMG 和姿势。使用姿势传感器识别外科医生处于静态和非要求姿势的时间窗口，然后在这些时间窗口内计算平均功率频率（MPF）。在 72 次暴露中的 57 次（80%）中，参与者经历了肌肉骨骼不适的增加。与单模态（sEMG）测量相比，集成（多模态）测量在检测疲劳预测指标 MPF 下降方面表现出更好的性能。基于肌肉骨骼不适的自我报告，提出了用于识别斜方肌和三角肌群组疲劳的基于传感器的阈值。工作相关疲劳是肌肉骨骼疾病的中间风险因素之一。本文提出了一种使用可穿戴传感器识别肌肉骨骼疲劳的客观综合方法。该方法可以由人体工程学家实施，以更准确地识别各种工作场所的肌肉骨骼疲劳。sEMG：表面肌电图；IMU：惯性测量单元；MPF：平均功率频率；ACGIH：美国政府工业卫生学家会议；SAGES：美国胃肠内镜外科医师学会；LD：左三角肌；LT：左斜方肌；RD：右三角肌；RT：右斜方肌。
