Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 6 (2015), Issue 1, Pages 1-22
http://dx.doi.org/10.1260/2040-2295.6.1.1
Research Article

An Exploratory Study on a Chest-Worn Computer for Evaluation of Diet, Physical Activity and Lifestyle

Mingui Sun,1,6,8 Lora E. Burke,2 Thomas Baranowski,3 John D. Fernstrom,4 Hong Zhang,5 Hsin-Chen Chen,1 Yicheng Bai,6 Yuecheng Li,1 Chengliu Li,6 Yaofeng Yue,6 Zhen Li,1 Jie Nie,1 Robert J. Sclabassi,7 Zhi-Hong Mao,6,8 and Wenyan Jia1

1Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, USA
2Department of Health and Community Systems, University of Pittsburgh, Pittsburgh, PA, USA
3Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
4Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
5Center for Image Processing, Beihang University, Beijing, China
6Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
7Computational Diagnostics, Inc., Pittsburgh, PA, USA
8Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

Received 1 January 2014; Accepted 1 December 2014

Copyright © 2015 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. A. Hindorff, P. Sethupathy, H. A. Junkins et al., “Potential etiologic and functional implications of genome-wide association loci for human diseases and traits,” Proceedings of the National Academy of Sciences, vol. 106, no. 23, pp. 9362–9367, 2009. View at Google Scholar
  2. P. Lichtenstein, N. V. Holm, P. K. Verkasalo et al., “Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland,” The New England Journal of Medicine, vol. 343, no. 2, pp. 78–85, 2000. View at Google Scholar
  3. W. C. Willett, “Balancing life-style and genomics research for disease prevention,” Science, vol. 296, no. 5568, pp. 695–698, 2002. View at Google Scholar
  4. S. M. Rappaport and M. T. Smith, “Epidemiology. environment and disease risks,” Science, vol. 330, no. 6003, pp. 460–461, 2010. View at Google Scholar
  5. S. M. Rappaport, “Implications of the exposome for exposure science,” Journal of Exposure Science and Environmental Epidemiology, vol. 21, no. 1, pp. 5–9, 2011. View at Google Scholar
  6. K. M. Flegal, M. D. Carroll, C. L. Ogden, and L. R. Curtin, “Prevalence and trends in obesity among US adults, 1999-2008,” JAMA, vol. 303, no. 3, pp. 235–241, 2010. View at Google Scholar
  7. “Overweight and obesity: a major public health issue,” U.S. Department of Health and Human Services. 2001. Available: http://odphp.osophs.dhhs.gov/pubs/prevrpt/01fall/pr.htm.
  8. R. A. Hammond and R. Levine, “The economic impact of obesity in the United States,” Journal of Diabetes, Metabolic Syndrome and Obesity, vol. 3, pp. 285–295, 2010. View at Google Scholar
  9. Magic. Pure Magic http://mealsnap.com/.
  10. C. J. Boushey, D. A. Kerr, J. Wright, K. D. Lutes, D. S. Ebert, and E. J. Delp, “Use of technology in children's dietary assessment,” European Journal of Clinical Nutrition, vol. 63, Suppl 1, pp. S50–S57, 2009. View at Google Scholar
  11. C. D. Lee, J. Chae, T. E. Schap et al., “Comparison of known food weights with image-based portion-size automated estimation and adolescents' self-reported portion size,” Journal of Diabetes Science and Technology, vol. 6, no. 2, pp. 428–434, 2012. View at Google Scholar
  12. B. L. Daugherty, T. E. Schap, R. Ettienne-Gittens et al., “Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents,” Journal of Medical Internet Research, vol. 14, no. 2, Article ID e58, 2012. View at Google Scholar
  13. I. M. Lee and E. J. Shiroma, “Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges,” British Journal of Sports Medicine, vol. 48, no. 3, pp. 197–201, 2014. View at Google Scholar
  14. Y. Bai, W. Jia, H. Zhang, Z. H. Mao, and M. Sun, “Helping the blind to find the floor of destination in multistory buildings using a barometer,” in Proceedings of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4738–4741, 2013.
  15. W. Zhang, W. Jia, and M. Sun, “Segmentation for efficient browsing of chronical video recorded by a wearable device,” in Proceedings of 36th Northeast Biomedical Engineering Conference, 2010.
  16. Z. Li, Z. Wei, W. Jia, and M. Sun, “Daily life event segmentation for lifestyle evaluation based on multi-sensor data recorded by a wearable device,” in Proceedings of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2858–2861, 2013.
  17. W. Jia, H. C. Chen, Y. Yue et al., “Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera,” Public Health Nutrition, vol. 17, no. 8, pp. 1671–1681.
  18. J. K. A. Ahuja, J. B. Montville, G. Omolewa-Tomobi et al., [Online]. USDA Food and Nutrient Database for Dietary Studies, 5. 0. U.S. Department of Agriculture, Agricultural Research Service, Food Surveys Research Group, Beltsville, MD.
  19. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Google Scholar
  20. J. Nie, Z. Wei, W. Jia et al., “Automatic detection of dining plates for image-based dietary evaluation,” in Proceedings of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4312–4315, 2010.
  21. J. Nie, J. D. Fernstrom, R. J. Sclabassi et al., “Automatic detection of dining plates in digital video,” in Proceedings of 36th Northeast Biomedical Engineering Conference, 2010.
  22. X. Sun, H. Yao, W. Jia, and M. Sun, “Eating activity detection from images acquired by a wearable camera,” in Proceedings of ACM SenseCam and Pervasive Imaging, pp. 80–81, 2013.
  23. F. Perazzi, P. Krahenbuhl, Y. Pritch, and A. Hornung, “. Saliency filters: contrast based filtering for salient region detection,” in Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 723–740, 2012.
  24. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254–1259.
  25. H. C. Chen, W. Jia, Y. Yue et al., “Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration,” Measurement Science and Technology, vol. 24, no. 10, Article ID 105701, 2013. View at Google Scholar
  26. Z. Levi and C. Gotsman, “D-Snake: image registration by as-similar-as-possible template deformation,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 2, pp. 331–343, 2013. View at Google Scholar
  27. S. JHaO, “A four-step camera calibration procedure with implicit image correction,” Proceedings of Computer Vision and Pattern Recognition, pp. 1106–1112, 1997. View at Google Scholar
  28. R. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE Journal of Robotics and Automation, pp. 323–344, 1987. View at Google Scholar
  29. Z. Zhang, “Flexible camera calibration by viewing a plane from unknown orientations,” in Proceedings of IEEE International Conference on Computer Vision, pp. 666–673, 1999.
  30. K. F. Janz, “Physical activity in epidemiology: moving from questionnaire to objective measurement,” British Journal of Sports Medicine, vol. 40, no. 3, pp. 191–192.
  31. N. J. Wareham and K. L. Rennie, “The assessment of physical activity in individuals and populations: Why try to be more precise about how physical activity is assessed?” International Journal of Obesity, vol. 22, pp. S30–S38, 1998. View at Google Scholar
  32. J. Kerr, S. J. Marshall, S. Godbole et al., “Using the SenseCam to improve classifications of sedentary behavior in free-living settings,” American Journal of Preventive Medicine, vol. 44, no. 3, pp. 290–296, 2013. View at Google Scholar
  33. ActiGraph Service: What are counts?. Accessed July 29, 2014 https://help.theactigraph.com/entries/20723176-what-are-counts.
  34. B. E. Ainsworth, W. L. Haskell, M. C. Whitt et al., “Compendium of physical activities: an update of activity codes and MET intensities,” Medicine & Science in Sports & Exercise, vol. 32, 9 Suppl, pp. S498–S504, 2000. View at Google Scholar
  35. B. E. Ainsworth, W. L. Haskell, S. D. Herrmann et al., “Compendium of physical activities: a second update of codes and MET values,” Medicine & Science in Sports & Exercise, vol. 43, no. 8, pp. 1575–1581, 2011. View at Google Scholar
  36. Z. Li, Study on analytic methods for human physical activity recognition based on wearable systems [Ph.D. thesis], Ocean University of China, 2013.
  37. C. Burges, “A tutorial on support vector machines for pattern recognition,” Journal of Data Mining and Knowledge Discovery, vol. 2, pp. 121–167, 1998. View at Google Scholar
  38. D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011. View at Google Scholar
  39. W. Jia and M. Sun, An event-based approach to daily physical activity evaluation using mobile devices. presented at the 2011 mHealth Summit, Washington DC, 2011.
  40. Y. Bai, A wearable indoor navigation system for the blind and visually impaired individuals [Ph.D. thesis], University of Pittsburgh, 2014.
  41. “PandaCare: Demo Preparing,” Accessed July 29, 2014 https://blogs.cornell.edu/cornellcup2013pandacare/.
  42. T. Baranowski, N. Islam, J. Baranowski et al., “Comparison of a Web-Based versus Traditional Diet Recall among Children,” Journal of the Academy of Nutrition and Dietetics, vol. 112, no. 4, pp. 527–532, 2012. View at Google Scholar