Influence of BMI on Elastographic Strain Ratios of Achilles Tendon
Keywords:Elastography, Body Mass Index, Ultrasound, Achilles tendon, stiffness
The Achilles tendon have two major problems due to injury; one being a chronic injury called Achilles tendinopathy and the second being acute injury which are more commonly known as Achilles tendon rupture. Changes in stiffness of Achilles tendon is alarming and can cause deleterious effects on quality of life in an individual. Achilles tendon is reported to be affected significantly due to the weight of an individual. The effect of Body Mass Index (BMI) on stiffness of Achilles tendon was evaluated in the current study. Elastography was performed on individuals ranging from 19 to 23 years for detecting the stiffness of the Achilles tendon. Individuals were grouped according to their BMI in 3 categories (underweight, normal and overweight) and their strain ratios were measured. The strain ratio results for all volunteers were ranging from 1.03 to 6 (1.03 for underweight and 6 for overweight). Difference in weight of individuals effect the Achilles tendon stiffness. The overweight individuals had the highest stiffness while the underweight individuals had the lowest. It is concluded that higher stiffness may likely lead to Achilles tendon injury.
(1) Shahid S. Higher Order Statistics Techniques Applied to EMG Signal Analysis and Characterization. Ph.D. thesis, University of Limerick; Ireland, 2004.
(2) Nikias CL, Raghuveer MR. Bispectrum estimation: A digital signal processing framework. IEEE Proceedings on Communications and Radar. 1987; 75 (7):869–891.
(3) Basmajian JV, de Luca CJ. Muscles Alive - The Functions Revealed by Electromyography. The Williams & Wilkins Company; Baltimore, 1985.
(4) Cram JR, Kasman GS, Holtz J. Introduction to Surface Electromyography. Aspen Publishers Inc.; Gaithersburg, Maryland, 1998.
(5) Thexton AJ. A randomization method for discriminating between signal and noise in recordings of rhythmic electromyographic activity. J Neurosci Meth. 1996; 66: 93 –98.
(6) Bornato P, de Alessio T, Knaflitz M. A statistical method for the measurement of the muscle activation intervals from surface myoelectric signal gait. IEEE Trans Biomed Eng.1998; 45: 287–299. doi: 10.1109/10.661154.
(7) Merlo A, Farina D. A Fast and Reliable Technique for Muscle Activity Detection from Surface EMG Signals. IEEE Trans Biomed Eng. 2003; 50 (3):316–323. Doi: 10.1109/TBME.2003.808829.
(8) Gabor D. Theory of communication. J Inst Elect Eng.1946; 93:429–457.
(9) Hefftner G, Zucchini W, Jaros G. The electromyogram (EMG) as a control signal for functional neuro-muscular stimulation part 1: Autoregressive modeling as a means of EMG signature discrimination. IEEE Trans Biomed Eng.1988; 35:230–237. doi: 10.1109/10.1370.
(10) Christodoulou CI, Pattichis CS. A new technique for the classification and decomposition of EMG signals. Proceedings in IEEE International Conference on Neural Networks.1995; 5:2303–2308.
(11) H. Arieta, R. Katoh, H. Yokoi, Y. Wenwei, 2006Development of a multi-DOF electromyography prosthetic system using the adaptive joint mechanism, ABBI 2006, 32110.