Ergonomic assessment of office worker postures using 3D automated joint angle assessment. (April 2022)

• Record Type: Journal Article
• Authors: Rodrigues, Patrick B.; Xiao, Yijing; Fukumura, Yoko E.; Awada, Mohamad; Aryal, Ashrant; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn C.
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Ergonomic assessment -- RULA -- Engineering office environments -- Depth camera -- Computer vision -- Machine learning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101596
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: 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 3D A utomated J oint Angle A ssessment, 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.