SVM-based waist circumference estimation using Kinect

doi:10.1016/j.cmpb.2020.105418

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SVM-based waist circumference estimation using Kinect

2020-07-01

DOI：10.1016/j.cmpb.2020.105418

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Abstract

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Background and objective: Conventional anthropometric studies using Kinect depth sensors have concen-trated on estimating the distances between two points such as height. This paper deals with a novel waist measurement method using SVM regression, further widening spectrum of Kinect's potential ap-plications. Waist circumference is a key index for the diagnosis of abdominal obesity, which has been linked to metabolic syndromes and other related diseases. Yet, the existing measuring method, tape mea-sure, requires a trained personnel and is therefore costly and time-consuming. Methods: A dataset was constructed by recording both 30 frames of Kinect depth image and careful tape measurement of 19 volunteers by a clinical investigator. This paper proposes a new SVM regressor-based approach for estimating waist circumference. A waist curve vector is extracted from a raw depth image using joint information provided by Kinect SDK. To avoid overfitting, a data augmentation tech-nique is devised. The 30 frontal vectors and 30 backside vectors, each sampled for 1 s per person, are combined to form 900 waist curve vectors and a total of 17,100 samples were collected from 19 indi-viduals. On an individual basis, we performed leave-one-out validation using the SVM regressor with the tape measurement-gold standard of waist circumference measurement-values labeled as ground-truth. On an individual basis, we performed leave-one-out validation using the SVM regressor with the tape measurement-gold standard of waist circumference measurement-values labeled as ground-truth. Results: The mean error of the SVM regressor was 4.62 cm, which was smaller than that of the geometric estimation method. Potential uses are discussed. Conclusions: A possible method for measuring waist circumference using a depth sensor is demonstrated through experimentation. Methods for improving accuracy in the future are presented. Combined with other potential applications of Kinect in healthcare setting, the proposed method will pave the way for patient-centric approach of delivering care without laying burdens on patients. (C) 2020 The Authors. Published by Elsevier B.V.

Keywords:

Support vector machine

Waist measurement

Machine learning

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