Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37212, USA.
Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37212, USA.
Sensors (Basel). 2021 Jan 6;21(2):340. doi: 10.3390/s21020340.
(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling.
(1) 背景：由于腰部重复受力和过度用力，下背部疾病是导致体力劳动者因重复性腰部负荷和过度用力而缺勤和身体残疾的主要原因。通常进行人体工程学评估，以了解和减轻肌肉骨骼过度用力损伤的风险。用于监测下背部负荷的可穿戴传感器解决方案有可能提高人体工程学评估的质量、数量和效率，并为过度用力损伤风险的个性化、连续监测提供机会。然而，现有的使用单个惯性测量单元 (IMU) 的可穿戴解决方案在估计不同质量物体处理时的背部负荷方面的准确性有限，而科学文献中的替代解决方案需要如此多的分布式传感器，以至于它们不适合广泛的工作场所实施。因此，我们探索了使用少量可穿戴传感器准确监测下背部负荷的新方法。(2) 方法：我们从实验室仪器和可穿戴传感器同步收集数据，以分析 10 名个体分别执行约 400 种不同的物料搬运任务。我们探索了数十种使用不同身体部位和/或压力鞋垫上的 IMU 的候选解决方案。(3) 结果：我们发现，准确监测下背部负荷的两个关键传感器是躯干 IMU 和压力鞋垫。使用这两个传感器的信号以及梯度提升决策树算法，有可能提供一种实用（传感器相对较少）、准确（高达 r = 0.89）且自动化（使用可穿戴设备）的方法来监测广泛的物料搬运任务中的时间序列腰椎力矩。躯干 IMU 可以用大腿 IMU 或骨盆 IMU 代替，而不会牺牲太多准确性，但压力鞋垫没有实用的替代品。在现实世界中，使用这种方法实现准确的腰椎负荷估计的关键将是优化压力鞋垫的力估计。(4) 结论：在这里，我们提出了一种有前途的可穿戴解决方案，用于在手动物料搬运过程中实用、自动化和准确地监测下背部负荷。
