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Reseach Article

Using Kinect Sensor for Detecting Early Symptoms of Disease using 3D Model from an Infrared Depth Sensor

Published on July 2016 by Kamalnayan Seth, Durgesh Tiwari, Rohit Sonar, Pankaj Mudholkar
International Conference on Advances in Information Technology and Management
Foundation of Computer Science USA
ICAIM2016 - Number 3
July 2016
Authors: Kamalnayan Seth, Durgesh Tiwari, Rohit Sonar, Pankaj Mudholkar
00ba83dd-e14a-4bd3-b0c8-8cf26c36e35f

Kamalnayan Seth, Durgesh Tiwari, Rohit Sonar, Pankaj Mudholkar . Using Kinect Sensor for Detecting Early Symptoms of Disease using 3D Model from an Infrared Depth Sensor. International Conference on Advances in Information Technology and Management. ICAIM2016, 3 (July 2016), 28-30.

@article{
author = { Kamalnayan Seth, Durgesh Tiwari, Rohit Sonar, Pankaj Mudholkar },
title = { Using Kinect Sensor for Detecting Early Symptoms of Disease using 3D Model from an Infrared Depth Sensor },
journal = { International Conference on Advances in Information Technology and Management },
issue_date = { July 2016 },
volume = { ICAIM2016 },
number = { 3 },
month = { July },
year = { 2016 },
issn = 0975-8887,
pages = { 28-30 },
numpages = 3,
url = { /proceedings/icaim2016/number3/25518-1690/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Information Technology and Management
%A Kamalnayan Seth
%A Durgesh Tiwari
%A Rohit Sonar
%A Pankaj Mudholkar
%T Using Kinect Sensor for Detecting Early Symptoms of Disease using 3D Model from an Infrared Depth Sensor
%J International Conference on Advances in Information Technology and Management
%@ 0975-8887
%V ICAIM2016
%N 3
%P 28-30
%D 2016
%I International Journal of Computer Applications
Abstract

Several chronic disease affects nearly 1. 7 billion people worldwide and over 750 million survivors are at-risk for developing diseases at some point in their life. Early detection of symptoms and management of these symptoms can significantly reduce the potential for symptoms and complications and a mechanism for handling it ; hothe studyver, many patients do not knowabout these symptoms and fail to seek medical assistance at the first sign of the disease which is most crucial stage . In this reaserch paper, the study will present a method for measuring bone density and for detecting early symptoms associated with several disease . The propose system relies on IR imaging sensors, such as in the Microsoft Kinect in xbox 360 . This technique will allow for the future development of tools for self-management and specialist monitoring using machine leraning , and when compared to other commercially available devicesin the market , our system is least complicated ,less expensive, or more reliable/accurate, fast forecaster and much more user friendly for the user .

References
  1. P. Mortimer, "Chronic peripheral oedema: the critical role of the lymphatic system. " Clinical Medicine, vol. 4(5), pp. 448–453, 2004.
  2. International Society of Lymphology, "The diagnosis and treatment of peripheral lymphedema: consensus document of the international society of lymphology," Lymphology, vol. 36, p. 9, 2003.
  3. J. M. Armer, M. E. Radina, D. Porock, and S. D. Culbertson, "Predicting breast cancer-related lymphedema using self-reported symptoms," Nurs Res, vol. 52(6), pp. 370–09, 2003.
  4. M. M. Hull, "Functional and psychosocial aspects of lymphedema in women treated for breast cancer," Innovations in Breast Cancer Care, vol. 3(4), pp. 97–100, 1998.
  5. G. Jager, "Quality-of-life and body image impairments in patients with lymphedema," Lymphology, vol. 39, pp. 193–200, 2006. ?
  6. J. A. Petrek and M. C. Heelan, "Incidence of breast carcinoma-related lymphedema," Cancer, vol. 83(12 Suppl American), pp. 2774–2781, 1998.
  7. E. G. Poage, "Lymphedema: What interventions are effective in reduc- ing risk for and treating secondary lymphedema?" Oncology Nursing Society, 2008.
  8. S. H. Ridner, L. D. Montgomery, J. T. Hepworth, B. R. Stewart, and J. M. Armer, "Comparison of upper limb volume measurement techniques and arm symptoms betthe studyen healthy volunteers and individuals with known lymphedema," Lymphology, vol. 40, no. 1, pp. 35–46, March 2007.
  9. J. M. Armer and B. R. Stewart, "A comparison of four diagnostic criteria for lymphedema in a post-breast cancer population," Lymphatic Research and Biology, vol. 3, no. 4, pp. 208–217, 2005.
  10. A. Sagen, R. Karesen, P. Skaane, and M. A. Risberg, "Validity for the simplified water displacement instrument to measure arm lymphedema as a result of breast cancer surgery," Arch Phys Med Rehabil, vol. 90, pp. 803–809, May 2009.
  11. R. Taylor, U. W. Jayasinghe, L. Koelmeyer, O. Ung, and J. Boyages, "Reliability and validity of arm volume measurements for assessment of lymphedema," Phys Ther, vol. 86, pp. 205–214, Feb 2006.
  12. A. G. Warren, B. A. Janz, S. A. Slavin, and L. J. Borud, "The use of bioimpedance analysis to evaluate lymphedema," Annals of Plastic Surgery, vol. 58, no. 5, 2007.
  13. A. W. Stanton, J. W. Northfield, B. Holroyd, P. S. Mortimer, and J. R. Levick, "Validation of an optoelectronic limb volumeter (perometer)," Lymphology, vol. 30, no. 77, 1997.
  14. J. Tong, J. Zhou, L. Liu, Z. Pan, and H. Yan, "Scanning 3d full human bodies using kinects," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 4, pp. 643–650, april 2012.
  15. B. Allen, B. Curless, and Z. Popovic, "The space of human body shapes: reconstruction and parameterization from range scans. " ACM Transactions on Graphics, vol. 22, no. 3, pp. 587–594, 2003.
  16. P. J. Besl and N. D. McKay, "A method for registration of 3D shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239–256, February 1992.
  17. D. Lam, R. Hong, and G. N. DeSouza , "3D human modeling using virtual multi-view stereopsis and motion estimation," in Proceedings of the 2009 IEEE International Conference on Intelligent Robots and Systems, Oct. 2009, pp. 4294–4299.
Index Terms

Computer Science
Information Sciences

Keywords

Kinect Sensor Symptoms machine Leraning Patients Management Of Symptoms.