RESEARCH ARTICLE


Prediction of Bodyweight and Energy Expenditure Using Point Pressure and Foot Acceleration Measurements



Nadezhda A Sazonova1, Raymond Browning2, Edward S. Sazonov3, *
1 Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
2 Department of Health and Exercise Science, Colorado State University, Fort Collins, CO 80523-1582, USA
3 Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA


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© Sazonova et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA; Tel: (205) 348-1981; Fax: (205) 348-6959; E-mail: esazonov@eng.ua.edu.


Abstract

Bodyweight (BW) is an essential outcome measure for weight management and is also a major predictor in the estimation of daily energy expenditure (EE). Many individuals, particularly those who are overweight, tend to underreport their BW, posing a challenge for monitors that track physical activity and estimate EE. The ability to automatically estimate BW can potentially increase the practicality and accuracy of these monitoring systems. This paper investigates the feasibility of automatically estimating BW and using this BW to estimate energy expenditure with a footwear-based, multisensor activity monitor. The SmartShoe device uses small pressure sensors embedded in key weight support locations of the insole and a heel-mounted 3D accelerometer. Bodyweight estimates for 9 subjects are computed from pressure sensor measurements when an automatic classification algorithm recognizes a standing posture. We compared the accuracy of EE prediction using estimated BW compared to that of using the measured BW. The results show that point pressure measurement is capable of providing rough estimates of body weight (root-mean squared error of 10.52 kg) which in turn provide a sufficient replacement of manually-entered bodyweight for the purpose of EE prediction (root-mean squared error of 0.7456 METs vs. 0.6972 METs). Advances in the pressure sensor technology should enable better accuracy of body weight estimation and further improvement in accuracy of EE prediction using automatic BW estimates.

Keywords: Bodyweight estimation, energy expenditure, pressure sensors, SmartShoe, wearable devices..