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.

工人经常因手动举起重物而出现腰痛。基于仪器的评估工具用于定量评估举重活动中的生物力学风险。本研究旨在验证与传统双极肌电图相比，高密度表面肌电图 (HDsEMG) 可以优化区分与不同疲劳提升条件相关的风险水平的假设。15 名参与者执行了三个提升任务，每个任务持续 15 分钟，提升指数 (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 的幅度可以显着区分每对 LI。从任务的第四分钟开始，HDsEMG 数据可以区分不同的 LI，而双极 sEMG 只能在任务结束时这样做。HDsEMG 数据在起重任务中的更高辨别力使这种方法成为一种有价值的工具，可用于监测起重时的疲劳，并可以扩展当前可用的基于仪器的工具提供的可能性。