首页|Investigators at University of Tubingen Describe Findings in Machine Learning [Virtual Reality (Vr) As a Testing Bench for Consumer Optical Solutions: a Machin e Learning Approach (Gbr) To Visual Comfort Under Simulated Progressive Addition ...]
Investigators at University of Tubingen Describe Findings in Machine Learning [Virtual Reality (Vr) As a Testing Bench for Consumer Optical Solutions: a Machin e Learning Approach (Gbr) To Visual Comfort Under Simulated Progressive Addition ...]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news originating from Tubingen, Germany, by NewsRx correspondents, research stated, "For decades, manufacturers have attemp ted to reduce or eliminate the optical aberrations that appear on the progressiv e addition lens' surfaces during manufacturing. Besides every effort made, some of these distortions are inevitable given how lenses are fabricated, where in fa ct, astigmatism appears on the surface and cannot be entirely removed, or where non-uniform magnification becomes inherent to the power change across the lens." Financial support for this research came from European Grant PLATYPUS, Marie Skl odowska-Curie RISE initiative. Our news journalists obtained a quote from the research from the University of T ubingen, "Some presbyopes may refer to certain discomfort when wearing these len ses for the first time, and a subset of them might never adapt. Developing, prot otyping, testing and purveying those lenses into the market come at a cost, whic h is usually reflected in the retail price. This study aims to test the feasibil ity of virtual reality (VR) for testing customers' satisfaction with these lense s, even before getting them onto production. VR offers a controlled environment where different parameters affecting progressive lens comforts, such as distorti ons, image displacement or optical blurring, can be inspected separately. In thi s study, the focus was set on the distortions and image displacement, not taking blur into account. Behavioural changes (head and eye movements) were recorded u sing the built-in eye tracker. We found participants were significantly more dis pleased in the presence of highly distorted lens simulations." According to the news editors, the research concluded: "In addition, a gradient boosting regressor was fitted to the data, so predictors of discomfort could be unveiled, and ratings could be predicted without performing additional measureme nts."
TubingenGermanyEuropeCyborgsEmer ging TechnologiesMachine LearningUniversity of Tubingen
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