Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040, Rome, Italy; Department of Engineering, Roma Tre University, Via Vito Volterra 62, Roma, Lazio, Italy.
Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040, Rome, Italy.
Appl Ergon. 2021 Sep;95:103456. doi: 10.1016/j.apergo.2021.103456. Epub 2021 May 11.
Workers often develop low back pain due to manually lifting heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk in lifting activities. This study aims to verify the hypothesis that high-density surface electromyography (HDsEMG) allows an optimized discrimination of risk levels associated with different fatiguing lifting conditions compared to traditional bipolar sEMG. 15 participants performed three lifting tasks with a progressively increasing lifting index (LI) each lasting 15 min. Erector spinae (ES) activity was recorded using both bipolar and HDsEMG systems. The amplitude of both bipolar and HDsEMG can significantly discriminate each pair of LI. HDsEMG data could discriminate across the different LIs starting from the fourth minute of the task while bipolar sEMG could only do so towards the end. The higher discriminative power of HDsEMG data across the lifting tasks makes such methodology a valuable tool to be used to monitor fatigue while lifting and could extend the possibilities offered by currently available instrumental-based tools.
工人在手动搬运重物时经常会出现腰痛。仪器评估工具用于定量评估举重活动中的生物力学风险。本研究旨在验证一个假设，即高密度表面肌电图（HDsEMG）比传统双极 sEMG 更能优化区分与不同疲劳举重条件相关的风险水平。15 名参与者进行了三项举重任务，每次持续 15 分钟，举重指数（LI）逐渐增加。使用双极和 HDsEMG 系统记录竖脊肌（ES）活动。双极和 HDsEMG 的幅度都可以显著区分每一对 LI。HDsEMG 数据可以从任务的第四分钟开始区分不同的 LI，而双极 sEMG 只能在接近任务结束时才能做到。HDsEMG 数据在举重任务中的更高区分能力使这种方法成为一种有价值的工具，可用于监测举重时的疲劳，并可扩展当前可用的仪器工具提供的可能性。
