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Dry Electrode-Based Body Fat Estimation System with Anthropometric Data for Use in a Wearable Device

1
The Department of Electrical and Electronic Engineering, Yonsei University, Shinchon-dong, Seodaemun-gu, Seoul 03722, Korea
2
The Department of Sport Industry Studies, Yonsei University, Shinchon-dong, Seodaemun-gu, Seoul 03722, Korea
3
The Faculty of Kinesiology, Sport, and Recreation, University of Alberta, 1-115 University Hall, 116 St. and 85 Ave., Edmonton, AB T6G 2R3, Canada
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2177; https://doi.org/10.3390/s19092177
Received: 25 February 2019 / Revised: 28 April 2019 / Accepted: 5 May 2019 / Published: 10 May 2019
(This article belongs to the Collection Wearable and Unobtrusive Monitoring Systems)
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Abstract

The bioelectrical impedance analysis (BIA) method is widely used to predict percent body fat (PBF). However, it requires four to eight electrodes, and it takes a few minutes to accurately obtain the measurement results. In this study, we propose a faster and more accurate method that utilizes a small dry electrode-based wearable device, which predicts whole-body impedance using only upper-body impedance values. Such a small electrode-based device typically needs a long measurement time due to increased parasitic resistance, and its accuracy varies by measurement posture. To minimize these variations, we designed a sensing system that only utilizes contact with the wrist and index fingers. The measurement time was also reduced to five seconds by an effective parameter calibration network. Finally, we implemented a deep neural network-based algorithm to predict the PBF value by the measurement of the upper-body impedance and lower-body anthropometric data as auxiliary input features. The experiments were performed with 163 amateur athletes who exercised regularly. The performance of the proposed system was compared with those of two commercial systems that were designed to measure body composition using either a whole-body or upper-body impedance value. The results showed that the correlation coefficient ( r 2 ) value was improved by about 9%, and the standard error of estimate (SEE) was reduced by 28%. View Full-Text
Keywords: bioelectrical impedance analysis; deep learning; percent body fat; upper-body measurement; settling value estimation bioelectrical impedance analysis; deep learning; percent body fat; upper-body measurement; settling value estimation
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Shin, S.-C.; Lee, J.; Choe, S.; Yang, H.I.; Min, J.; Ahn, K.-Y.; Jeon, J.Y.; Kang, H.-G. Dry Electrode-Based Body Fat Estimation System with Anthropometric Data for Use in a Wearable Device. Sensors 2019, 19, 2177.

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