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 (r2)
value was improved by about 9%, and the standard error of estimate (SEE) was reduced by 28%.