Prisco Giuseppe, Pirozzi Maria Agnese, Santone Antonella, Cesarelli Mario, Esposito Fabrizio, Gargiulo Paolo, Amato Francesco, Donisi Leandro
Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy.
Diagnostics (Basel). 2025 Jan 4;15(1):105. doi: 10.3390/diagnostics15010105.
: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks. This study aimed to evaluate the potential of machine learning models, trained on postural sway metrics derived from an inertial measurement unit (IMU) placed at the lumbar region, to classify risk levels associated with load lifting based on the Revised NIOSH Lifting Equation. : To compute postural sway parameters, the IMU captured acceleration data in both anteroposterior and mediolateral directions, aligning closely with the body's center of mass. Eight participants undertook two scenarios, each involving twenty consecutive lifting tasks. Eight machine learning classifiers were tested utilizing two validation strategies, with the Gradient Boost Tree algorithm achieving the highest accuracy and an Area under the ROC Curve of 91.2% and 94.5%, respectively. Additionally, feature importance analysis was conducted to identify the most influential sway parameters and directions. : The results indicate that the combination of sway metrics and the Gradient Boost model offers a feasible approach for predicting biomechanical risks in load lifting. : Further studies with a broader participant pool and varied lifting conditions could enhance the applicability of this method in occupational ergonomics.
长期与工作相关的肌肉骨骼疾病主要受诸如提举负荷的持续时间、强度和重复性等因素影响。尽管传统的人体工程学评估工具可能有效，但由于缺乏简化的标准化框架，它们往往难以应用且复杂。最近，将可穿戴传感器与人工智能相结合已成为一种有效监测和减轻生物力学风险的有前景的方法。本研究旨在评估基于放置在腰椎区域的惯性测量单元（IMU）得出的姿势摆动指标训练的机器学习模型，根据修订后的美国国家职业安全与健康研究所（NIOSH）提举方程对与提举负荷相关的风险水平进行分类的潜力。
为了计算姿势摆动参数，IMU采集了前后方向和内外侧方向的加速度数据，这些数据与身体重心紧密对齐。八名参与者进行了两种场景测试，每种场景包括连续20次提举任务。使用两种验证策略对八个机器学习分类器进行了测试，梯度提升树算法分别达到了最高准确率，受试者工作特征曲线下面积分别为91.2%和94.5%。此外，还进行了特征重要性分析，以确定最有影响力的摆动参数和方向。
结果表明，摆动指标与梯度提升模型的结合为预测提举负荷中的生物力学风险提供了一种可行的方法。
对更广泛的参与者群体和不同提举条件进行进一步研究，可能会提高该方法在职业人体工程学中的适用性。
