Comparative Analysis of Imaging and Novel Markerless Approach for Measurement of Postural Parameters in Dental Seating Tasks

Bhatia, Vibha; Randhawa, Jagjit Singh; Jain, Ashish; Grover, Vishakha

doi:10.1007/978-981-15-5151-2_24

Cited by 1 publication

(1 citation statement)

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“…Instead of measuring the observer's eyes, postural information is automatically gathered using sensors or image recognition techniques to compensate for the drawbacks of observational approaches. A common approach is to collect postural data with Kinect TM and analyze work postures using observational approaches (Diego-Mas et al, 2014;Manghisi et al, 2017;Plantard et al, 2017;Bhatia et al, 2020). However, the accuracy of coordinate acquisition in Kinect TM varies based on the positional relationship between the sensor and the measured target.…”

Section: Introductionmentioning

confidence: 99%

Work postural ergonomic assessment using two-dimensional joint coordinates

Hida

OKADA

Matsumoto

2022

JAMDSM

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Work-related musculoskeletal disorders are the most frequent health issues, with awkward posture being one of the risk factors. Observational methods are often used in the manufacturing industry to analyze work postures in production fields. In this study, we present a straightforward technique for evaluating work postures utilizing the Ovako working posture analysis system (OWAS). The proposed technique calculates OWAS-based posture codes by manually acquiring employees' two-dimensional (2D) joint coordinates on the work image and inputting these coordinates into advanced machine learning models. Experiments were conducted to extract three-dimensional (3D) joint coordinates in the global coordinate system in the OWAS-based postures to develop machine learning models. Furthermore, the resulting 3D coordinates were converted to 2D joint coordinates in the camera image coordinate system using the direct linear transformation (DLT) method. The 2D joint coordinates and accompanying OWAS posture codes were utilized as training data to build machine learning models using the support vector machine algorithm. Cross validation confirmed the agreement rate of the OWAS action category (AC) by more than 80%, according to the experimental results.

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Section: Introductionmentioning

confidence: 99%