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  • 1
    Publication Date: 2019-05-10
    Description: 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%.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
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  • 2
    Publication Date: 2019-12-03
    Description: Background: This study aimed to test the validity of three different submaximal tests (i.e., 3-min step test with 20.3-cm step box height (3MST20), 3-min step test with 30-cm step box height (3MST30), and 6-min walk test (6MWT)) in estimating maximal oxygen consumption (VO2max) in young and healthy individuals. Methods: The 3MST20, 3MST30, 6MWT, as well as the cardiopulmonary exercise test (CPET) were performed in 73 participants (37 men and 36 women; mean age: 30.8 ± 9.3 years). All participants visited the clinic three in a random order for anthropometric measurements, three submaximal tests, and the VO2max test. Multiple linear regression analyses were conducted to construct the VO2max prediction equations for each submaximal test. Results: The prediction equations developed based on multiple regression analyses for each submaximal tests were as follows: 3MST20: VO2max = 86.0 − 10.9 × sex (male = 1, female = 2) − 0.4 × age − 0.1 × weight − 0.1 × heart rate recovery at 30 s (HRR30s); 3MST30: VO2max = 84.5 − 10.2 × sex (male = 1, female = 2) − 0.4 × age − 0.1 × weight − 0.1 × HRR30s; and 6MWT: VO2max = 61.1 − 11.1 × sex (male = 1, female = 2) − 0.4 × age − 0.2 × weight − 0.2 × (distance walked·10−1). The estimated VO2max values based on formulated equations were 37.0 ± 7.9, 37.3 ± 7.6, and 36.9 ± 7.9 mL∙kg−1∙min−1 derived from the 3MST20, 3MST30, and 6MWT, respectively. These estimated VO2max values were not significantly different from the measured VO2max value, 37.3 mL∙kg−1∙min−1. The estimated VO2max based on the 3MST20, 3MST30, and 6MWT results explained 73.4%, 72.2%, and 74.4% of the variances in the measured VO2max (p 〈 0.001), respectively. Conclusions: The 3MST20, 3MST30, and 6MWT were valid in estimating VO2max in relatively young and healthy Asian individuals.
    Print ISSN: 1661-7827
    Electronic ISSN: 1660-4601
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Medicine
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