| 000 | 03492nlm1a2200517 4500 | ||
|---|---|---|---|
| 001 | 661528 | ||
| 005 | 20231030041750.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\32151 | ||
| 035 | _aRU\TPU\network\30779 | ||
| 090 | _a661528 | ||
| 100 | _a20200110a2019 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aGB | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aPredicting body fat mass by IR thermographic measurement of skin temperature: a novel multivariate model _fG. Laffaye, V. V. Epishev, I. A. Tetin [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 330 | _aThe purpose of this study has been to develop a multivariate model for predicting body fat mass in women by using the technique of infrared (IR) thermography. Sixty-nine healthy women, aged from 16 to 29, were investigated by using a body composition analyser and IR thermographic temperature measurement. The correlation analysis was performed to reveal the problem of multicollinearity. The technique of principal component analysis (PCA) was applied in order to reduce the number of variables. Both the total fat mass and the fat mass in the torso were accepted as the dependent variables. The individual scores were used as independent variables on each component after applying the orthogonal rotation. Two datasets were analysed: the full dataset with anthropometric characteristics (age, body mass, body length) and without anthropometric characteristics. The stepwise model meeting the Akaike information criterion (AIC) was selected to estimate the relative quality of all models. The models obtained on the full dataset were able to explain 73.9% of the fat mass in the torso and 70.4% of the total fat mass. Respectively, the models based on the reduced dataset explained 52.5% of the fat mass in the torso and 51.5% of the total fat mass. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tQuantitative InfraRed Thermography Journal | ||
| 463 |
_tVol. 17, iss. 3 _v[P. 192-209] _d2019 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _ainfrared thermography | |
| 610 | 1 | _atemperature distribution | |
| 610 | 1 | _askin temperature | |
| 610 | 1 | _abody composition | |
| 610 | 1 | _aadipose tissue | |
| 610 | 1 | _agender | |
| 610 | 1 | _amultivariate model | |
| 610 | 1 | _aинфракрасная термография | |
| 610 | 1 | _aтемпература | |
| 610 | 1 | _aмногомерные модели | |
| 701 | 1 |
_aLaffaye _bG. |
|
| 701 | 1 |
_aEpishev _bV. V. |
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| 701 | 1 |
_aTetin _bI. A. |
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| 701 | 1 |
_aKorableva _bYu. B. |
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| 701 | 1 |
_aNaumova _bK. A. |
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| 701 | 1 |
_aAntonenko _bE. V. |
|
| 701 | 1 |
_aVavilov _bV. P. _cSpecialist in the field of dosimetry and methodology of nondestructive testing (NDT) _cDoctor of technical sciences (DSc), Professor of Tomsk Polytechnic University (TPU) _f1949- _gVladimir Platonovich _2stltpush _3(RuTPU)RU\TPU\pers\32161 |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет (ТПУ) _bИнститут неразрушающего контроля (ИНК) _bЛаборатория № 34 (Тепловых методов контроля) _h6591 _2stltpush _3(RuTPU)RU\TPU\col\19616 |
| 801 | 2 |
_aRU _b63413507 _c20200930 _gRCR |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1080/17686733.2019.1646449 |
| 942 | _cCF | ||