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

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}stainless steel electrode. The small size of this electrode has a narrow contact area with the skin; thus, it takes a very long time to obtain a stabilized impedance value. To address this problem, we propose a model-driven algorithm with a settling time of only five seconds. To improve the comfort of the measurement posture, we used the wrist of one hand and the index finger of the opposite hand as skin contact points [14]. The reason for choosing the index finger method is that the area where the left and right hands overlap with each other in the measurement process is smaller than in other conventional wearable devices [15]. In this method, the measured impedance value is highly influenced by the impedance value of the index finger; thus, it is not suitable to use the conventional cylinder-based model [8,16]. The cylinder-based model, whose target is determined by the value of $Heigh{t}^{2}/R$, where R denotes impedance, only utilizes information from the trunk, arms, and legs, but not that of the hands and feet. In this paper, we updated the $Heigh{t}^{2}/R$ prediction algorithm to reduce the effect caused by the index finger impedance. In addition, we estimated PBF by finding a mapping rule between the calibrated impedance value and the true PBF.It should be noted that the information obtainable from the upper-body impedance measurement system is insufficient to predict the whole-body PBF. To address this problem, waist and hip circumferences were used as additional input features. Typically, the mapping rule is implemented by conventional linear regression techniques [8,16], but we introduced a deep learning-based regression framework to achieve higher accuracy.

## 2. Related Works

#### 2.1. Various Types of Body Composition Measurement Methods

#### 2.2. Body Composition Measurement Using Bioelectrical Impedance Analysis (BIA)

#### 2.3. Effect of Measurement Position on BIA Method

^{2}, which makes it difficult to carry [13]. To increase portability and the ease of taking measurements, an upper-body device was developed as shown in Figure 3c. This technique measures the impedance value between the left and right hands [11]. Based on this, body composition is calculated with the same parameters used in the whole-body measurement method. The size of the device used to measure upper-body impedance is $197\times 49\times 128$ mm

^{3}, and it includes four electrodes, each of which is $35\times 40$ mm

^{2}. Because this device includes four large-sized electrodes, the time required for a measurement is only three seconds; however, it is also difficult to implement it in a wearable form. Finally, Figure 3d shows a wearable type of device to be designed for a portable device. It uses one hand’s wrist and the other hand’s thumb and index finger.

#### 2.4. Electrode Separation Effect between TX and RX

## 3. System Design and Data Acquisition Method

#### 3.1. Hardware Design and Measurement Posture

#### 3.2. Data Acquisition Process

^{2}electrodes depicted in Figure 7a,b, where we used a 50 kHz input current source, and impedance values were measured five times at one-second intervals. There was a 15-min break between each BIA measurement.

#### 3.3. Subject Statistics

#### 3.4. Comparison of the PBF Results of Whole-Body and Upper-Body Measurements

## 4. Input Feature Processing Methods

#### 4.1. Signal Feasibility Checker

#### 4.2. Settling Value Estimator

#### 4.3. Predicting the Value of ${H}^{2}/{R}_{50}$

#### 4.4. PBF Estimation Method Using Deep Neural-Networks

## 5. Results and Discussion

#### 5.1. Input Feature Calibration Results

#### 5.2. PBF Estimation Using Deep Neural Networks

_{conv}is a method based on features from previous studies using linear regression. L

_{prop}added waist circumference, hip circumference, and waist to hip ratio to the L

_{conv}technique. Figure 12 shows the comparison results between the reference and estimated PBF ratios for the training and test sets. The results showed that the L

_{conv}and L

_{prop}are similar in terms of performance, as shown in Figure 12a,b. As mentioned in Section 4.4, it was confirmed that the linear regression modeling technique does not represent the waist circumference and the hip circumference, which are assumed to present lower-body information. Yet, when DNN was applied, both training and test results improved. The results of the training set and the test set are shown in Figure 12c,d. The x-axis is the ground truth and the y-axis is the estimated output. From the training results shown in Figure 12c, it can be seen that the DNN technique has a higher correlation than the regression model. This confirms that DNN is more efficient when the anthropometric data proposed in this study are included. Further, the results of the test set which were not used in the process of network training are shown in Figure 12c. In this experiment, it was confirmed that the DNN technique shows a higher correlation value than the conventional regression technique.

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

BIA | Body impedance analysis |

PBF | Percent body fat |

SEE | Standard error of estimate |

DEXA | Dual-energy X-ray absorptiometry |

BMI | Body mass index |

TBW | Total body water |

FFM | Fat-free mass |

FM | Fat mass |

ReLU | Rectified linear unit |

## References

- Casselman, J.; Onopa, N.; Khansa, L. Wearable healthcare: Lessons from the past and a peek into the future. Telemat. Inform.
**2017**, 34, 1011–1023. [Google Scholar] [CrossRef] - Gutin, B.; Islam, S.; Manos, T.; Cucuzzo, N.; Smith, C.; Stachura, M.E. Relation of percentage of body fat and maximal aerobic capacity to risk factors for atherosclerosis and diabetes in black and white seven-to eleven-year-old children. J. Pediatr.
**1994**, 125, 847–852. [Google Scholar] [CrossRef] - Renehan, A.G.; Tyson, M.; Egger, M.; Heller, R.F.; Zwahlen, M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet
**2008**, 371, 569–578. [Google Scholar] [CrossRef] - Vazquez, G.; Duval, S.; Jacobs, D.R., Jr.; Silventoinen, K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: A meta-analysis. Epidemiol. Rev.
**2007**, 29, 115–128. [Google Scholar] [CrossRef] - Leahy, S.; O’Neill, C.; Sohun, R.; Jakeman, P. A comparison of dual energy X-ray absorptiometry and bioelectrical impedance analysis to measure total and segmental body composition in healthy young adults. Eur. J. Appl. Physiol.
**2012**, 112, 589–595. [Google Scholar] [CrossRef] - Rubiano, F.; Nunez, C.; Heymsfield, S. A comparison of body composition techniques. Ann. N. Y. Acad. Sci.
**2000**, 904, 335–338. [Google Scholar] [CrossRef] - Heyward, V.H.; Wagner, D.R. Applied Body Composition Assessment, 2nd ed.; Human Kinetics: Champaign, IL, USA, 2004. [Google Scholar]
- Lukaski, H.C.; Bolonchuk, W.W.; Hall, C.B.; Siders, W.A. Validation of tetrapolar bioelectrical impedance method to assess human body composition. J. Appl. Physiol.
**1986**, 60, 1327–1332. [Google Scholar] [CrossRef] - Malavolti, M.; Mussi, C.; Poli, M.; Fantuzzi, A.; Salvioli, G.; Battistini, N.; Bedogni, G. Cross-calibration of eight-polar bioelectrical impedance analysis versus dual-energy X-ray absorptiometry for the assessment of total and appendicular body composition in healthy subjects aged 21–82 years. Ann. Hum. Biol.
**2003**, 30, 380–391. [Google Scholar] [CrossRef] [PubMed] - Foster, K.R.; Lukaski, H.C. Whole-body impedance—What does it measure? Am. J. Clin. Nutr.
**1996**, 64, 388S–396S. [Google Scholar] [CrossRef] [PubMed] - Omron-Healthcare. Body Fat Monitor HBF-306. Available online: https://images-eu.ssl-images-amazon.com/images/I/91U2Mvk%2B9cS.pdf (accessed on 10 May 2019).
- InBody. Body Composition Analyzer Inbody-720. Available online: https://inbody.com/eng/product/inbody720.aspx (accessed on 10 May 2019).
- Tanita. Body Fat Scales. Available online: https://www.tanita.com/en/body-water-monitors-fat-scales/ (accessed on 10 May 2019).
- Jung, M.H.; Namkoong, K.; Lee, Y.; Koh, Y.J.; Eom, K.; Jang, H.; Bae, J.; Park, J. Wrist-wearable bioelectrical impedance analyzer with contact resistance compensation function. In Proceedings of the 2016 IEEE SENSORS, Orlando, FL, USA, 30 October–3 November 2016; pp. 1–3. [Google Scholar]
- Inbody. InBodyBAND2. Available online: https://www.inbody.com/global/product/InBodyBAND_2.aspx (accessed on 10 May 2019).
- Kyle, U.G.; Bosaeus, I.; De Lorenzo, A.D.; Deurenberg, P.; Elia, M.; Gómez, J.M.; Heitmann, B.L.; Kent-Smith, L.; Melchior, J.C.; Pirlich, M.; et al. Bioelectrical impedance analysis Part I: Review of principles and methods. Clin. Nutr.
**2004**, 23, 1226–1243. [Google Scholar] [CrossRef] [PubMed] - Jia, W.; Lu, J.; Xiang, K.; Bao, Y.; Lu, H.; Chen, L. Prediction of abdominal visceral obesity from body mass index, waist circumference and waist-hip ratio in Chinese adults: Receiver operating characteristic curves analysis. Biomed. Environ. Sci. BES
**2003**, 16, 206–211. [Google Scholar] [PubMed] - Brook, R.D.; Bard, R.L.; Rubenfire, M.; Ridker, P.M.; Rajagopalan, S. Usefulness of visceral obesity (waist/hip ratio) in predicting vascular endothelial function in healthy overweight adults. Am. J. Cardiol.
**2001**, 88, 1264–1269. [Google Scholar] [CrossRef] - Wang, J.; Thornton, J.C.; Russell, M.; Burastero, S.; Heymsfield, S.; Pierson, R.N., Jr. Asians have lower body mass index (BMI) but higher percent body fat than do whites: Comparisons of anthropometric measurements. Am. J. Clin. Nutr.
**1994**, 60, 23–28. [Google Scholar] [CrossRef] - Lukaski, H.C. Methods for the assessment of human body composition: Traditional and new. Am. J. Clin. Nutr.
**1987**, 46, 537–556. [Google Scholar] [CrossRef] [PubMed] - Durnin, J.; Rahaman, M.M. The assessment of the amount of fat in the human body from measurements of skinfold thickness. Br. J. Nutr.
**1967**, 21, 681–689. [Google Scholar] [CrossRef] [PubMed] - Heyward, V. ASEP methods recommendation: Body composition assessment. J. Exerc. Physiol. Online
**2001**, 4, 1–12. [Google Scholar] - Fürstenberg, A.; Davenport, A. Comparison of multifrequency bioelectrical impedance analysis and dual-energy X-ray absorptiometry assessments in outpatient hemodialysis patients. Am. J. Kidney Dis.
**2011**, 57, 123–129. [Google Scholar] [CrossRef] - Mialich, M.S.; Sicchieri, J.F.; Junior, A.A.J. Analysis of body composition: A critical review of the use of bioelectrical impedance analysis. Int. J. Clin. Nutr.
**2014**, 2, 1–10. [Google Scholar] - Segal, K.R.; Burastero, S.; Chun, A.; Coronel, P.; Pierson, R.N., Jr.; Wang, J. Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement. Am. J. Clin. Nutr.
**1991**, 54, 26–29. [Google Scholar] [CrossRef] [PubMed] - O’brien, C.; Young, A.; Sawka, M. Bioelectrical impedance to estimate changes in hydration status. Int. J. Sports Med.
**2002**, 23, 361–366. [Google Scholar] [CrossRef] - Talma, H.; Chinapaw, M.; Bakker, B.; HiraSing, R.; Terwee, C.; Altenburg, T. Bioelectrical impedance analysis to estimate body composition in children and adolescents: A systematic review and evidence appraisal of validity, responsiveness, reliability and measurement error. Obes. Rev.
**2013**, 14, 895–905. [Google Scholar] [CrossRef] - Haroun, D.; Taylor, S.J.; Viner, R.M.; Hayward, R.S.; Darch, T.S.; Eaton, S.; Cole, T.J.; Wells, J.C. Validation of bioelectrical impedance analysis in adolescents across different ethnic groups. Obesity
**2010**, 18, 1252–1259. [Google Scholar] [CrossRef] [PubMed] - Kyle, U.G.; Genton, L.; Karsegard, L.; Slosman, D.O.; Pichard, C. Single prediction equation for bioelectrical impedance analysis in adults aged 20–94 years. Nutrition
**2001**, 17, 248–253. [Google Scholar] [CrossRef] - Lohman, T.G. Advances in body composition assessment. Med. Sci. Sports Exerc.
**1993**, 25, 762. [Google Scholar] [CrossRef] - Kotler, D.P.; Burastero, S.; Wang, J.; Pierson, R. Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: Effects of race, sex, and disease. Am. J. Clin. Nutr.
**1996**, 64, 489S–497S. [Google Scholar] [CrossRef] - Deurenberg, P.; Leenen, R.; Weststrate, J.; Seidell, J. Sex and age specific prediction formulas for estimating body composition from bioelectrical impedance: A cross-validation study. Int. J. Obes.
**1991**, 15, 17–25. [Google Scholar] - Boulier, A.; Fricker, J.; Thomasset, A.L.; Apfelbaum, M. Fat-free mass estimation by the two-electrode impedance method. Am. J. Clin. Nutr.
**1990**, 52, 581–585. [Google Scholar] [CrossRef] - Stolarczyk, L.M.; Heyward, V.H.; Hicks, V.L.; Baumgartner, R.N. Predictive accuracy of bloelectrical impedance in estimating body composition of Native American women. Am. J. Clin. Nutr.
**1994**, 59, 964–970. [Google Scholar] [CrossRef] - Sun, S.S.; Chumlea, W.C.; Heymsfield, S.B.; Lukaski, H.C.; Schoeller, D.; Friedl, K.; Kuczmarski, R.J.; Flegal, K.M.; Johnson, C.L.; Hubbard, V.S. Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. Am. J. Clin. Nutr.
**2003**, 77, 331–340. [Google Scholar] [CrossRef] [PubMed] - Heitmann, B. Prediction of body water and fat in adult Danes from measurement of electrical impedance. A validation study. Int. J. Obes.
**1990**, 14, 789–802. [Google Scholar] - Choi, A.; Kim, J.Y.; Jo, S.; Jee, J.H.; Heymsfield, S.B.; Bhagat, Y.A.; Kim, I.; Cho, J. Smartphone-based bioelectrical impedance analysis devices for daily obesity management. Sensors
**2015**, 15, 22151–22166. [Google Scholar] [CrossRef] [PubMed] - Tomtom. Fitness Tracker. Available online: https://www.tomtom.com/en_us/sports/fitness-trackers/fitness-tracker-touch/black-large/ (accessed on 10 May 2019).
- Demura, S.; Sato, S.; Kitabayashi, T. Percentage of total body fat as estimated by three automatic bioelectrical impedance analyzers. J. Physiol. Anthropol. Appl. Hum. Sci.
**2004**, 23, 93–99. [Google Scholar] [CrossRef] - Chi, Y.M.; Jung, T.P.; Cauwenberghs, G. Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Rev. Biomed. Eng.
**2010**, 3, 106–119. [Google Scholar] [CrossRef] [PubMed] - Kingma, D.P.; Ba, J. Adam: A method for stochastic optimization. arXiv
**2014**, arXiv:1412.6980. [Google Scholar] - Strutz, T. Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and beyond; Vieweg and Teubner: New York, NY, USA, 2010. [Google Scholar]
- Nuzzo, R. Scientific method: Statistical errors. Nat. News
**2014**, 506, 150. [Google Scholar] [CrossRef]

**Figure 1.**Bioelectrical impedance analysis (BIA) measurement principle. The electrode is in contact with the skin, and the frequency of the current source differs by the purpose of the measurement. To obtain a stabilized constant impedance value, the measurement time needs to be set sufficiently long in general.

**Figure 2.**Measurement information and electrode configuration. Each measurement point has two electrodes. One is TX for current injection, and the other is RX for voltage sensing. (

**a**) Conventional measurement parameters: Five impedance measurements using eight electrodes; (

**b**) proposed measurement parameters: Five measurements using four electrodes.

**Figure 3.**Examples of commercial percent body fat (PBF) measurement devices using the BIA method. The devices can be classified into either whole-body or upper-body type depending on the measurement methods, and either hand-held or wearable type. (

**a**) Inbody-720: Inbody Co., Ltd. (Seoul, Korea), Whole-body impedance measurement, 8-electrode [12]; (

**b**) Scale type: Tanita Co., Ltd. (Tokyo, Japan), Whole-body impedance measurement, 8-electrode [13]; (

**c**) HBF-306: OMRON Co., Ltd. (Kyoto, Japan), Upper-body impedance measurement, 4-electrode [11]; (

**d**) Inbody band: Inbody Co., Ltd., Upper-body impedance, 4-electrode [15].

**Figure 4.**Modeling measurement parameters by the configuration of TX and RX. (

**a**) TX and RX sharing case; (

**b**) TX and RX separation case.

**Figure 5.**Block diagram of overall system structure. The input features consist of body information and impedance values measured for five seconds. The measured impedance values are calibrated to improve accuracy. Finally, a deep neural network (DNN) is used to predict PBF accurately.

**Figure 6.**The upper-body impedance-measurement method using a wearable device. This method uses four electrodes: Two as the current source (TX) and two to measure voltage (RX). One RX and one TX electrode are placed at each measurement site. (

**a**) wrist + two-finger type; (

**b**) wrist + one-finger type.

**Figure 7.**An example hardware design and measurement posture (

**a**) electrode configuration installed at the inside of the band consisting of TX1 and RX1 electrodes; (

**b**) electrode configuration installed at the outside of the band consisting of TX2 and RX2 electrodes; (

**c**) measurement posture using the device. Electrodes are connected to the external board via a wire which consists of a TX and RX circuit.

**Figure 8.**PBF measurement results using whole-body and upper-body measurements. The x-axis shows the results of whole-body measurements using Inbody-720. The y-axis shows the results of upper-body measurements using OMRON HBF-306.

**Figure 9.**TX system diagram. The feedback loop is formed around the body and the circuit. Therefore, the parasitic component is an important component to determine the characteristics of the feedback loop.

**Figure 10.**A method to estimate a settling value using initial measurement data (e.g., ${\Delta}_{12}$ = ${V}_{\delta}-{V}_{2\delta}$).

**Figure 11.**Finger impedance measurement data. The settling algorithm was applied to the measured values, and the impedance was calculated by subtracting the upper-body impedance measured using a large-sized electrode. There was no gender difference.

**Figure 12.**PBF estimation results for models based on regression and DNN. (

**a**) Correlation coefficients obtained from each method. For the label information, “L” means linear regression and “DNN” means deep neural network. The difference due to the input feature is distinguished by the “conv” and the “prop”. The “conv” is composed of the parameters used in the previous research, and “prop” is applied around the waist circumference and the hip circumference; (

**b**) standard error of estimate (SEE) values obtained from each method; (

**c**) training results obtained from 143 subjects; (

**d**) test results obtained from 20 unseen subjects. Commercial device data were taken from HBF-306.

Items | Unit | Inbody-720 (Reference Device) | HBF-306 | This Work |
---|---|---|---|---|

Measurement Point | Whole body | Upper body | Upper body | |

TX Frequency | kHz | 1, 5, 50, 250, 500, 1000 | 50 | 50 |

Electrode | EA | 8 | 4 | 4 |

Output Information | ICW ECW Dry Lean Mass Body Fat Mass | Body Fat Ratio | Body Fat Ratio | |

Accuracy (PBF) | % | 94% with DEXA [39] |

Summary | Basketball | Kendo | Ice Hockey | Baseball | Rugby | General | |
---|---|---|---|---|---|---|---|

102 | 9 | 13 | 10 | 22 | 32 | 16 | |

Height (cm) | 178.0 ± 7.2 | 190.0 ± 7.2 | 172.0 ± 7.7 | 176.9 ± 3.7 | 176.4 ± 4.7 | 179.6 ± 6.4 | 174.5 ± 5.7 |

Age (year) | 21.3 ± 3.1 | 20.3 ± 1.6 | 19.1 ± 0.6 | 20.1 ± 1.1 | 21.0 ± 3.1 | 20.1 ± 1.4 | 26.2 ± 3.0 |

Weight (kg) | 83.6 ± 13.0 | 84.6 ± 10.6 | 70.5 ± 8.3 | 81.7 ± 4.8 | 79.4 ± 10.4 | 93.1 ± 15.0 | 78.7 ± 9.8 |

Waist (cm) | 83.5 ± 7.8 | 79.0 ± 3.6 | 78.5 ± 5.2 | 83.1 ± 4.0 | 82.2 ± 7.7 | 86.2 ± 14.0 | 81.4 ± 6.7 |

Hip (cm) | 100.7 ± 6.4 | 97.8 ± 8.6 | 94.6 ± 4.5 | 102.8 ± 1.8 | 99.4 ± 5.2 | 104.4 ± 6.8 | 99.2 ± 5.0 |

BF (%) | 17.9 ± 5.9 | 10.0 ± 2.7 | 15.3 ± 3.7 | 18.1 ± 2.9 | 17.3 ± 4.2 | 21.6 ± 6.8 | 18.3 ± 5.1 |

BMI | 26.3 ± 3.5 | 23.3 ± 1.9 | 23.8 ± 1.8 | 26.2 ± 1.7 | 25.5 ± 3.1 | 28.8 ± 4.2 | 25.8 ± 2.6 |

Correlation* | 0.8385 | 0.0489 | 0.4925 | 0.6574 | 0.7690 | 0.8797 | 0.9491 |

Summary | Basketball | Kendo | Taekwondo | Judo | Table Tennis | General | |
---|---|---|---|---|---|---|---|

61 | 8 | 4 | 14 | 23 | 6 | 6 | |

Height (cm) | 165.2 ± 6.2 | 167.0 ± 4.6 | 166.0 ± 3.0 | 167.3 ± 5.1 | 163.8 ± 8.0 | 161.6 ± 3.5 | 166.9 ± 4.2 |

Age (year) | 20.2 ± 2.4 | 20.9 ± 1.7 | 20.0 ± 1.4 | 19.1 ± 1.2 | 20.0 ± 1.4 | 20.2 ± 1.3 | 24.6 ± 6.1 |

Weight (kg) | 65.4 ± 13.5 | 66.2 ± 9.5 | 62.6 ± 3.1 | 61.1 ± 9.4 | 72.0 ± 17.7 | 55.8 ± 6.0 | 62.4 ± 4.6 |

Waist (cm) | 74.2 ± 8.8 | 73.3 ± 6.5 | 71.8 ± 3.5 | 70.4 ± 6.9 | 77.9 ± 10.0 | 73.6 ± 5.9 | 73.7 ± 13.2 |

Hip (cm) | 97.7 ± 6.5 | 100.0 ± 5.9 | 95.0 ± 2.5 | 96.8 ± 5.4 | 99.8 ± 7.9 | 92.9 ± 5.0 | 95.4 ± 3.3 |

BF (%) | 25.2 ± 6.0 | 26.8 ± 7.6 | 21.4 ± 4.2 | 23.2 ± 5.4 | 26.8 ± 6.7 | 25.8 ± 4.8 | 23.5 ± 4.5 |

BMI | 24.1 ± 4.3 | 24.9 ± 5.2 | 22.7 ± 1.7 | 21.8 ± 3.3 | 26.6 ± 4.6 | 21.4 ± 2.3 | 23.0 ± 1.6 |

Correlation* | 0.9201 | 0.9479 | 0.9909 | 0.9086 | 0.9337 | 0.9344 | 0.7773 |

Network 1 | Network 2 | |
---|---|---|

(${\mathit{H}}^{2}/{\mathit{R}}_{50}$) | (PBF) | |

Training Set | 143 (male: 93, female: 50) | |

Test Set | 20 (male: 10, female: 10) | |

Input layer | 7 | 8 |

Hidden layer | 3 | 3 |

Output layer | 1 | 1 |

Hidden node | 128 × 128 × 128 | 256 × 256 × 256 |

Activation function | ReLU | |

Optimizer | Adam Optimizer [41] (beta1 = 0.9, beta2 = 0.999, epsilon = 1 × 10^{$-8$}) | |

Cost function | Mean square error (MSE) | |

Number of training cycles | maximum = 2000 (Early stopping adopted) |

Conventional Features | Proposed Features | |||
---|---|---|---|---|

Coefficient | p-Value | Coefficient | p-Value | |

Intercept | 59.6240 | <0.0001 | −16.2929 | 0.6774 |

Age | −0.0992 | 0.2170 | −0.1302 | 0.1059 |

Gender | 7.4604 | <0.0001 | 7.5189 | <0.0001 |

Height | −0.4174 | <0.0001 | −0.4005 | <0.0001 |

Weight | 0.4673 | <0.0001 | 0.3763 | <0.0001 |

${H}^{2}/{R}_{50}$ | −0.4217 | <0.0001 | −0.4021 | <0.0001 |

Waist | – | – | −0.6804 | 0.1224 |

Hip | – | – | 0.7177 | 0.0674 |

Waist to Hip | – | – | 78.5731 | 0.0837 |

Case | Methods | Input Dimension | Detailed Information |
---|---|---|---|

Conventional | Regression | 5 | H, A, G, W, ${R}_{50}$_5 |

Estimated Impedance | Regression | 5 | H, A, G, W, ${R}_{50}$_prop |

Model I | DNN (Network1) | 5 | H, A, G, W, ${R}_{50}$_prop |

Model II | DNN (Network1) | 6 | H, A, G, W, ${R}_{50}$_prop, H${}^{2}$/Imp |

Model III | DNN (Network1) | 7 | H, A, G, W, ${R}_{50}$_prop, H${}^{2}$/Imp, W/H |

^{2}/Imp: $Heigh{t}^{2}/{R}_{50}\_prop$, W/H: waist to hip ratio.

Conventional | Estimated Imp. | Model I | Model II | Model III | |
---|---|---|---|---|---|

Correlation* | 0.8420 | 0.9008 | 0.9344 | 0.9338 | 0.9403 |

Improvement [%] | - | 37.2 | 58.4 | 58.1 | 62.2 |

Initial | Methods | Input Dim. | Detailed Information |
---|---|---|---|

L_{conv} | Regression | 5 | H, A, G, W, H^{2}/${R}_{50}$ |

L_{prop} | Regression | 8 | H, A, G, W, H^{2}/${R}_{50}$, hip Circ., waist Circ., waist/hip |

DNN_{conv} | DNN (Network2) | 5 | H, A, G, W, $\widehat{{H}^{2}/{R}_{50}}$ |

DNN_{prop} | DNN (Network2) | 8 | H, A, G, W, $\widehat{{H}^{2}/{R}_{50}}$, hip Circ., waist Circ., waist/hip |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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.
https://doi.org/10.3390/s19092177

**AMA Style**

Shin S-C, Lee J, Choe S, Yang HI, Min J, Ahn K-Y, Jeon JY, Kang H-G.
Dry Electrode-Based Body Fat Estimation System with Anthropometric Data for Use in a Wearable Device. *Sensors*. 2019; 19(9):2177.
https://doi.org/10.3390/s19092177

**Chicago/Turabian Style**

Shin, Seung-Chul, Jinkyu Lee, Soyeon Choe, Hyuk In Yang, Jihee Min, Ki-Yong Ahn, Justin Y. Jeon, and Hong-Goo Kang.
2019. "Dry Electrode-Based Body Fat Estimation System with Anthropometric Data for Use in a Wearable Device" *Sensors* 19, no. 9: 2177.
https://doi.org/10.3390/s19092177