Background:The purpose of this study was to use motion capture to collect body posture information during simulated endoscopic sinus surgery interventions performed by both specialists and residents in standing and si ing positions and to analyze that information with the validated Rapid Upper Limb Assessment (RULA) tool, which allows calculation of a risk index of musculoskeletal overload. Methods:Bilateral endoscopic sinus surgery was performed in 5 cadaver heads by 2 residents, and 4 practicing rhinologists. Musculoskeletal symptoms were evaluated before and a er the dissection. Full-body postural data were collected with the help of Kinect and a .NET WPF (Windows Presentation Foundation) so ware application to record images of the surgical procedures, and then analyzed with the RULA tool to calculate a risk score indicative of the exposure of the individual surgeon to ergonomic risk factors associated with upper extremity musculoskeletal disorders.Results: All subjects reported physical discomfort after nasal endoscopic procedures. An overall similar RULA score was obtained by the residents and the practicing rhinologists. The RULA score was slightly lower for the sitting position than for the standing position, mostly due to a lower score in group B (neck, trunk, and leg); however, the RULA score for group A (arm and wrist analysis) was higher, denoting a higher risk for the upper back and arms.Conclusion: Significant musculoskeletal symptoms were reported a er an endoscopic operation by both the resident and the practicing otolaryngologists. All surgeons obtained a high RULA score, meaning that urgent changes are required in the task. C 2019 ARS-AAOA, LLC. How to Cite this Article:Lobo D, Anuarbe P, López-Higuera JM, Viera J, Castillo N, Megía R. Estimation of surgeons' ergonomic dynamics with a structured light system during endoscopic surgery. Int Forum Allergy Rhinol. 2019;9:857-864.
A method for the automatic synthesis of the Ultraviolet-Visible-Near Infrared (UV-Vis-NIR) absorption and transmission spectra of dye mixtures based on the absorption characteristics of their individual dyes is proposed in this paper. Multiple Linear Regression models (MLR) of each dye are obtained using a fiber-optic set-up operating in the 200-1100 nm wavelength range. Textile dyes are thick and dense and, consequently, optically opaque. This gives rise to high absorbance values which do not permit the direct comparison of different textile dye spectra for quality assurance purposes. The proposed multivariate method allows to construct a general model adjusted to the number and concentration of the dyes in the mixture. Furthermore, this dye spectrum synthesis can provide the optimum dilution factor needed to compare two different dye mixtures and test their similarity degree. With the proposed system, the obtained spectral correlation coefficients between the measured and synthesized spectra of a dye mixture are greater than 99% for both the transmission and absorption spectra.
In this paper, an extrinsic optical fibre sensor (OFS) for the quantitative determination of dyes used in the textile industry is presented. The system proposed is based on absorption spectroscopy and multivariate calibration methods to infer the concentration of different textile dyes. The performance of the sensor has been successfully assessed using calibrated dyes, with a very good correlation between the multivariate calibration models and the predicted values. The sensor system here demonstrated could be used to predict the colour of dye mixtures during the dyebath and, therefore, reduce the manufacturing costs.
An extrinsic optical fiber sensor method for color matching assessment of textile dyes using their Ultraviolet-Visible-Near Infrared (UV-Vis-NIR) transmission spectra is proposed in this paper. Spectra values are converted to CIELAB coordinates to improve the calculation of color differences. Color matching assessment is finally performed using Receiver Operating Characteristic (ROC) curves to find the optimum discrimination threshold between different dye samples.
A method for the unsupervised clustering of optically thick textile dyes based on their spectral properties is demonstrated in this paper. The system utilizes optical fibre sensor techniques in the Ultraviolet-Visible-Near Infrared (UV-Vis-NIR) to evaluate the absorption spectrum and thus the colour of textile dyes. A multivariate method is first applied to calculate the optimum dilution factor needed to reduce the high absorbance of the dye samples. Then, the grouping algorithm used combines Principal Component Analysis (PCA), for data compression, and K-means for unsupervised clustering of the different dyes. The feasibility of the proposed method for textile applications is also discussed in the paper.
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