Measurement methods for body fat (%) assessment from 3D Kinect scans

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dc.contributor.advisorSadakata, M.
dc.contributor.advisorBoer, S. den
dc.contributor.authorAlexa, A.
dc.contributor.otherPhilips Research Europeen_US
dc.date.issued2016-09-29
dc.description.abstractBody composition is a better health indicator than just scale weighting. Previous research internally conducted at Philips showed that a model based on full 3D body representation can accurately measure body fat(%) (RMSE = 2.22%) on pregnant women. The current study investigates the plausibility of performing body fat assessment from single 3D depth-maps, thus an incomplete3D representation of human body. A Kinect v2 device was used for data acquisition and two predictive models based on hand crafted features extracted from the point cloud data have been developed. A Lasso regression lead to a 3 parameter fat prediction model with ad j 􀀀 R2 = 0.72 and RMSE = 8.02%. A multivariate linear regression with a stepwise elimination routine resulted into a predictive model with adj-R2 = 0.60, and RMSE = 9.85%.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/4385
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleMeasurement methods for body fat (%) assessment from 3D Kinect scansen_US
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