一种使用深度神经网络评估身体质量指数的无骨骼kinect系统

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 1900-01-01 DOI:10.1109/SYSENG.2017.8088252

D. Nahavandi, A. Abobakr, H. Haggag, M. Hossny, S. Nahavandi, D. Filippidis

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引用次数: 16

摘要

在本文中，我们提出了一个无骨骼Kinect系统来估计人体的身体质量指数(BMI)。与文献中的其他系统不同，所提出的系统不需要秤来测量重量。使用体表面积(BSA)回归估计观察对象的体重。该系统采用最先进的深度残差网络来提取有意义的特征，并以95%的准确率估计BMI分数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

2017 IEEE International Systems Engineering Symposium (ISSE)

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