Ergonomic assessment of office worker postures using 3D automated joint angle assessment
Ergonomic assessment of office worker postures using 3D automated joint angle assessment
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DOI：
10.1016/j.aei.2022.101596
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发表时间：
2022-04
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通讯作者：
Patrick B. Rodrigues;Yijing Xiao;Yoko E Fukumura;Mohamad Awada;Ashrant Aryal;B. Becerik-Gerber;Gale M. Lucas;Shawn C Roll
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文献类型：
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作者：
Patrick B. Rodrigues;Yijing Xiao;Yoko E Fukumura;Mohamad Awada;Ashrant Aryal;B. Becerik-Gerber;Gale M. Lucas;Shawn C Roll
久坐活动和静态姿势与工作相关的肌肉骨骼疾病（WMSDs）和工人不适有关。对办公室工作人员的人体工程学评估通常由专家使用快速上肢评估（Rapid Upper Limb Assessment，简称RIESS）等工具进行，但只有有限的证据表明持续遵守专家的建议。通过实时、连续的反馈，评估一天中的姿势变化并识别不良姿势将受益于自动化。存在自动姿势评估方法;然而，它们通常基于可能限制用户的姿势、服装和发型的理想条件，或者可能需要参与者的无障碍视图。使用微软Kinect摄像头和开源计算机视觉算法，我们提出了一个自动化的人体工程学评估算法，以监测办公室工作人员的姿势，3DAutomatedJoint AngleAssessment，3D-AJA。3D-AJA的有效性进行了测试，通过比较算法计算的关节角度的角度从手动测角和Kinect软件开发工具包（SDK）的20名参与者在办公空间。评估结果表明，3D-AJA的肩关节屈曲、肩关节外展和肘关节屈曲相对于测角法测量的关节角度的平均绝对误差范围为5.6° ± 5.1°至8.5° ± 8.1°。此外，3D-AJA在使用随机森林模型的Risk评分A的分类方面表现出相对良好的性能（微平均F1评分= 0.759，G平均值= 0.811），即使在受试者下肢的高水平闭塞时也是如此。研究结果为开发适合办公室人员的全身工效学评估提供了依据，该评估可以支持个性化的行为改变，帮助办公室人员调整姿势，从而降低WMSD的风险。
Sedentary activity and static postures are associated with work-related musculoskeletal disorders (WMSDs) and worker discomfort. Ergonomic evaluation for office workers is commonly performed by experts using tools such as the Rapid Upper Limb Assessment (RULA), but there is limited evidence suggesting sustained compliance with expert’s recommendations. Assessing postural shifts across a day and identifying poor postures would benefit from automation by means of real-time, continuous feedback. Automated postural assessment methods exist; however, they are usually based on ideal conditions that may restrict users’ postures, clothing, and hair styles, or may require unobstructed views of the participants. Using a Microsoft Kinect camera and open-source computer vision algorithms, we propose an automated ergonomic assessment algorithm to monitor office worker postures, the 3DAutomatedJoint AngleAssessment, 3D-AJA. The validity of the 3D-AJA was tested by comparing algorithm-calculated joint angles to the angles obtained from manual goniometry and the Kinect Software Development Kit (SDK) for 20 participants in an office space. The results of the assessment show that the 3D-AJA has mean absolute errors ranging from 5.6° ± 5.1° to 8.5° ± 8.1° for shoulder flexion, shoulder abduction, and elbow flexion relative to joint angle measurements from goniometry. Additionally, the 3D-AJA showed relatively good performance on the classification of RULA score A using a Random Forest model (micro averages F1-score = 0.759, G-mean = 0.811), even at high levels of occlusion on the subjects’ lower limbs. The results of the study provide a basis for the development of a full-body ergonomic assessment for office workers, which can support personalized behavior change and help office workers to adjust their postures, thus reducing their risks of WMSDs.