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

^{1}

^{2}

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## 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 |

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**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 |

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## 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