19 April 2017 Detection and characterization of exercise induced muscle damage (EIMD) via thermography and image processing
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Abstract
Exercise induced muscle damage (EIMD), is usually experienced in i) humans who have been physically inactive for prolonged periods of time and then begin with sudden training trials and ii) athletes who train over their normal limits. EIMD is not so easy to be detected and quantified, by means of commonly measurement tools and methods. Thermography has been used successfully as a research detection tool in medicine for the last 6 decades but very limited work has been reported on EIMD area. The main purpose of this research is to assess and characterize EIMD, using thermography and image processing techniques. The first step towards that goal is to develop a reliable segmentation technique to isolate the region of interest (ROI). A semi-automatic image processing software was designed and regions of the left and right leg based on superpixels were segmented. The image is segmented into a number of regions and the user is able to intervene providing the regions which belong to each of the two legs. In order to validate the image processing software, an extensive experimental investigation was carried out, acquiring thermographic images of the rectus femoris muscle before, immediately post and 24, 48 and 72 hours after an acute bout of eccentric exercise (5 sets of 15 maximum repetitions), on males and females (20-30 year-old). Results indicate that the semi-automated approach provides an excellent bench-mark that can be used as a clinical reliable tool.
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N. P. Avdelidis, N. P. Avdelidis, V. Kappatos, V. Kappatos, G. Georgoulas, G. Georgoulas, P. Karvelis, P. Karvelis, C. K. Deli, C. K. Deli, P. Theodorakeas, P. Theodorakeas, G. Giakas, G. Giakas, A. Tsiokanos, A. Tsiokanos, M. Koui, M. Koui, A. Z. Jamurtas, A. Z. Jamurtas, } "Detection and characterization of exercise induced muscle damage (EIMD) via thermography and image processing", Proc. SPIE 10171, Smart Materials and Nondestructive Evaluation for Energy Systems 2017, 101710R (19 April 2017); doi: 10.1117/12.2261278; https://doi.org/10.1117/12.2261278
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