Skip to main content
Log in

Measurement of subcutaneous adipose tissue thickness by near-infrared

  • Scientific Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Obesity is strongly associated with the risks of diabetes and cardiovascular disease, and there is a need to measure the subcutaneous adipose tissue (SAT) layer thickness and to understand the distribution of body fat. A device was designed to illuminate the body parts by near-infrared (NIR), measure the backscattered light, and predict the SAT layer thickness. The device was controlled by a single-chip microcontroller (SCM), and the thickness value was presented on a liquid crystal display (LCD). There were 30 subjects in this study, and the measurements were performed on 14 body parts for each subject. The paper investigated the impacts of pressure and skin colour on the measurement. Combining with principal component analysis (PCA) and support vector regression (SVR), the measurement accuracy of SAT layer thickness was 89.1 % with a mechanical caliper as reference. The measuring range was 5–11 mm. The study provides a non-invasive and low-cost technique to detect subcutaneous fat thickness, which is more accessible and affordable compared to other conventional techniques. The designed device can be used at home and in community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Li C, Ford ES, McGuire LC et al (2007) Increasing trends in waist circumference and abdominal obesity among U.S. adults. Obesity 15(1):216–224

    Article  PubMed  Google Scholar 

  2. Aldhafiri F, Al-Nasser A, Al-Sugair A et al (2012) Obesity and metabolic syndrome in adolescent survivors of standard risk childhood acute lymphoblastic leukemia in Saudi Arabia. Pediatr Blood Cancer 59(1):133–137

    Article  PubMed  Google Scholar 

  3. Kuller LH (2006) Nutrition, lipids, and cardiovascular disease. Nutr Rev 64(2):15–26

    Article  Google Scholar 

  4. Thomas C, Hypponen E, Power C (2006) Type 2 diabetes mellitus in midlife estimated from the Cambridge risk score and body mass index. Arch Intern Med 166:682–688

    Article  PubMed  Google Scholar 

  5. Wang J, Thornton JC, Kolesnik S et al (2000) Anthropometry in body composition: an overview. Annals of the New York academy of sciences, 1st (edn) edn. Wiley, New York, pp 317–326

    Google Scholar 

  6. Siervo M, Jebb SA (2010) Body composition assessment: theory into practice: introduction of multicompartment models. IEEE Eng M 29(1):48–59

    Article  Google Scholar 

  7. Brodie D, Moscrip V, Hutcheon R (1998) Body composition measurement: a review of hydrodensitometry, anthropometry, and impedance methods. Nutrition 14(3):296–310

    Article  PubMed  CAS  Google Scholar 

  8. Chae YS, Jeong MG, Kim D (2007) Three dimensional volume measurement of mouse abdominal fat in magnetic resonance images. In: 9th international conference on e-health networking, application and services, pp 252–255

  9. Leinhard OD, Johansson A, Rydell J et al (2008) Quantitative abdominal fat estimation using MRI. In: 19th international conference on pattern recognition, ICPR, pp 1–4

  10. Ng JG, Rohling R, Lawrence PD (2009) Automatic measurement of human subcutaneous fat with ultrasound. IEEE Ultras 56(8):1642–1653

    Article  Google Scholar 

  11. Cursino CMP, Galvao RRA, Freire RCS et al (2009) Subcutaneous fat tissue thickness measurement based on ultrasound. In: I2MTC ‘09 IEEE pp 284–287

  12. Ritz P, Salle′ A, Audran M, Rohmer V (2007) Comparison of different methods to assess body composition of weight loss in obese and diabetic patients. Diabet RE C 77:405–411

    CAS  Google Scholar 

  13. Trebbels D, Fellhauer F, Jugl M et al (2012) Online tissue discrimination for transcutaneous needle guidance applications using broadband impedance spectroscopy. IEEE Biomed 59(2):494–503

    Article  Google Scholar 

  14. Kinoshita M, Aoki H, Koshiji K (2007) Basic study on fat thickness estimation using electrical bio-impedance tomography. ITAB pp 91–94

  15. Kim K, Lee M, Kim J et al (2009) Performance evaluation of the electrode configuration in bioelectrical impedance analysis for visceral fat measurement. In: 31st annual international conference of the IEEE EMBS Minneapolis, Minnesota, 2–6 September 2009, pp 892–895

  16. Surovy NJ, Billah MM, Haowlader S et al (2012) Determination of abdominal fat thickness using dual electrode separation in the focused impedance method (FIM). Physiol Meas 33:707–718

    Article  PubMed  Google Scholar 

  17. Hwang JS (2007) Local body fat measurement device and method of operating the same. US patent 20070239070

  18. Petrucelli S (2008) Device for detecting and displaying one or more of body weight, body fat percentage, blood pressure, pulse and environmental temperature. US patent 20080183398

  19. KIM YB, BAE YS (2007) Non-invasive measuring apparatus of subcutaneous fat thickness and method thereof. WO 2008/029978 A1, PCT/KR2007/000981

  20. Hwang ID, Shin K, Ho DS et al (2006) Evaluation of chip LED sensor module for fat thickness measurement using tissue phantoms. In: proceedings of the 28th IEEE EMBS annual international conference, New York, Aug 30–Sept 3, pp 5993–5996

  21. Tafeit E, Möller R, Sudi K et al (2000) Artifical neural networks as a method to improve the precision of subcutaneous adipose tissue thickness measurements by means of the optical device Lipometer. Comput Biol 30:355–365

    Article  CAS  Google Scholar 

  22. Jürimäe T, Jürimäe J, Wallner SJ et al (2007) Relationships between body fat measured by DXA and subcutaneous adipose tissue thickness measured by Lipometer in adults. J Physiol Anthropol 26(4):513–516

    Article  PubMed  Google Scholar 

  23. Jürimä T, Sudi K, Jürimä J et al (2005) Validity of optical device Lipometer and bioelectric impedance analysis for body fat assessment in men and women. Coll Antropol 29(2):499–502

    Google Scholar 

  24. Hwang ID, Shin K (2007) Fat thickness measurement using optical technique with miniaturized chip LEDs. A preliminary human study. In: proceedings of the 29th annual international conference of the IEEE EMBS Cité Internationale, Lyon, 2–26 August 2007 pp 4548–4551

  25. Song WJ, Zhang S, Yang YM et al (2009) The system of portable fat detector with dual-wavelength near-infrared light. In: ICBBE, pp 1–4

  26. Sone S, Niwayama M, Shinohara S (2007) The influence of probe geometry on muscle oxygenation measurement using spatially-resolved NIRS SICE, 2007 Annual Conference Sept 17–20, 2007, Kagawa University, Japan, pp 716–719

  27. Zhu JD, Ding HS, Wang PY, Liu GH (1996) A new noninvasive NIR approach for the estimation of body. Beijing Biomed Eng 15(1):1–3

    Google Scholar 

  28. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inform Process–Lett Rev 11(10):203–224

    Google Scholar 

  29. Bland J, Altman DG (1995) Comparing methods of measurement: why plotting difference against standard method is misleading. The lancet 346:1085–1087

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Dongmei Hao or Yanjun Zeng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Y., Yang, Z., Hao, D. et al. Measurement of subcutaneous adipose tissue thickness by near-infrared. Australas Phys Eng Sci Med 36, 201–208 (2013). https://doi.org/10.1007/s13246-013-0196-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-013-0196-y

Keywords

Navigation