Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.
Sensors (Basel). 2020 Mar 11;20(6):1557. doi: 10.3390/s20061557.
Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures ( < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed ( < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.
通过实时测量生物力学参数进行工效学评估，在减少非致命性职业伤害（如与工作相关的肌肉骨骼疾病）方面具有巨大潜力。保持正确的姿势可以确保避免背部和下肢承受高压力，而不正确的姿势会增加脊柱的压力。在这里，我们提出了一种通过可穿戴传感器和机器学习算法以及运动学数据来识别姿势模式的解决方案。26 名健康受试者配备了 8 个无线惯性测量单元（IMU），进行了手动搬运任务，例如提起和放下小负载，采用两种姿势：正确和不正确。通过生物力学模型从 IMU 测量中估计运动学参数的测量值，例如下肢和腰骶关节的运动范围，以及相对于骨盆的躯干位移。在正确和不正确的姿势之间，所有运动学参数都存在统计学差异（<0.01）。此外，在提升任务中随着负载重量的增加，观察到髋关节和躯干运动学的变化（<0.01）。为了自动识别这两种姿势，我们训练了一种监督机器学习算法，即支持向量机，使用所有运动学参数作为特征，达到了 99.4%的准确率（特异性为 100%）。同时，使用与躯干体段相关的运动学参数的测量值，达到了 76.9%的准确率（特异性为 76.9%）。
