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A skeleton-free kinect system for body mass index assessment using deep neural networks

Version 2 2024-06-04, 02:20
Version 1 2018-07-29, 14:09
conference contribution
posted on 2024-06-04, 02:20 authored by D Nahavandi, A Abobakr, H Haggag, M Hossny, S Nahavandi, D Filippidis
© 2017 IEEE. In this paper we present a skeleton-free Kinect system to estimate body mass index (BMI) of human bodies. Unlike other systems in the literature, the proposed system does not require a scale to measure the weight. The weight of observed subjects are estimated using body surface area (BSA) regression. The proposed system employs the state-of-the-art deep residual network to extract meaningful features and estimate the BMI scores with a 95% accuracy.

History

Location

Vienna, Austria

Start date

2017-10-11

End date

2017-10-13

ISBN-13

9781538634035

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2017, IEEE

Title of proceedings

ISSE 2017 : Proceedings of the IEEE International Symposium on Systems Engineering

Event

Systems Engineering. International Symposium (2017 : Vienna, Austria)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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