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Forward and inverse dynamic study during pedaling: Comparison between the young and the elderly

Abstract

BACKGROUND:

As it is not easy to investigate various variables that affect exercise efficacies and cause injuries while pedaling in the actual experiment, especially for the elderly, the musculoskeletal model simulation with a comparison of measured electromyography (EMG) data could be used to minimize experimental trials.

OBJECTIVE:

The measured EMG data were compared with the muscle activities from the musculoskeletal model through forward (FD) and inverse dynamic (ID) analysis.

METHODS:

EMG was measured from eight young adult (20's) and eight elderly (70's) in three minutes pedaling with a constant load and 40 revolutions per minute (RPM) cadence. The muscles used for the analysis were the VastusLateralis, Tibialis Anterior, Bicep Femoris, and Gastrocnemius Medial. Pearson's correlation coefficients of the muscle activity patterns, on-off set, and peak timing at the maximum muscle activity were calculated and compared. BIKE3D and GaitLowerExtremity model were used for the FD and ID simulation.

RESULTS:

There are significant correlations in the muscle activity patterns except in the case of Biceps Femoris muscle by ID. Thus, it can be concluded that muscle activities of model & EMG showed similar results.

CONCLUSION:

The result shows that it could be possible to use the musculoskeletal model for various pedaling simulations.

References

[1] 

Kim IK, , Jung MH. The effect of ride a bicycle on the physical fitness of an old person, Journal of Physical Education & Sports Science, 1996; 14(1): 115-124.

[2] 

Rasmussen J, , Damsgaard M, , Christensen ST. Simulation of tendon energy storage in pedaling, MEDICON 2001: Proceedings of the international federation for medical & emp; Biological engineering, 2001; 4: 7391-7395.

[3] 

Shin YH, , Choi JS, , Kang DW, , Seo JW, , Lee JH, , Kim JY, et al. A study on human musculoskeletal model for cycle fitting: comparison with EMG, International Journal of Medical, Health, Pharmaceutical and Biomedical Engineering, 2015; 9: 43-47.

[4] 

McCartney N, , Hicks AL, , Martin J, , Webber CE. Long-term Resistance Training in the Elderly: Effects on Dynamic Strength, Exercise Capacity, Muscle, and Bone, The Journal of Gerontology: Biological Sciences, 1995; 50(2): 97-104.

[5] 

Albertus-Kajee Y, , Tucker R, , Derman W, , Lambert M. Alternative methods for normalizing EMG during cycling, Journal of Electromyography and Kinesiology, 2010; 20(6): 1036-1043.

[6] 

Brian SB, , Li L. Lower extremity muscle activities during cycling are influenced by load and frequency, Journal of Electromyography and Kinesiology, 2003; 13(2): 181-190.

[7] 

Seo JW, , Kang DW, , Kim JY, , Yang ST, , Kim DH, , Choi JS, , Tack GR. Finite element analysis of the femur during stance phase of gait based on musculoskeletal model simulation, Bio-Medical Materials and Engineering, 2014; 24(6): 2485-2493.

[8] 

Mestdagh K. Personal perspective: in search of an optimum cycling posture, Applied Ergonomics, 1998; 29(5): 325-334.

[9] 

Sacchetti M, , Lenti M, , Di Palumbo AS, , De Vito G. Different effect of cadence on cycling efficiency between young and older cyclists, Medicine & Science in Sports & Exercise, 2010; 42(11): 2128-2133.

[10] 

Damsgaard M, , Rasmussen J, , Christensen ST, , Surma E, , Zee M. Analysis of musculoskeletal systems in the AnyBody modeling system, Simulation Modelling Practice and Theory, 2006; 14(8): 1100-1111.

[11] 

Jacobs R, , Bobbert MF, , van Ingen Schenau GJ. Function of mono- and biarticular muscles in running, Medicine and Science in Sports and Exercise, 1993; 25(10): 1163-1173.