 Abstract—It is difficult to study the effect of various variables on cycle fitting through actual experiment. To overcome such difficulty, the forward dynamics of a musculoskeletal model was applied to cycle fitting in this study. The measured EMG data weres compared with the muscle activities of the musculoskeletal model through forward dynamics. EMG data were measured from five cyclists who do not have musculoskeletal diseases during three minutes pedaling with a constant load (150 W) and cadence (90 RPM). The muscles used for the analysis were the Vastus Lateralis (VL), Tibialis Anterior (TA), Bicep Femoris (BF), and Gastrocnemius Medial (GM). Person’s correlation coefficients of the muscle activity patterns, the peak timing of the maximum muscle activities, and the total muscle activities were calculated and compared. BIKE3D model of AnyBody (Anybodytech, Denmark) was used for the musculoskeletal model simulation. The comparisons of the actual experiments with the simulation results showed significant correlations in the muscle activity patterns (VL: 0.789, TA: 0.503, BF: 0.468, GM: 0.670). The peak timings of the maximum muscle activities were distributed at particular phases. The total muscle activities were compared with the normalized muscle activities, and the comparison showed about 10% difference in the VL (+10%), TA (+9.7%), and BF (+10%), excluding the GM (+29.4%). Thus, it can be concluded that muscle activities of model & experiment showed similar results. The results of this study indicated that it was possible to apply the simulation of further improved musculoskeletal model to cycle fitting. Keywords—Cycle fitting, EMG, Musculoskeletal modeling, Simulation. I. INTRODUCTION HE cycling population is explosively increasing, keeping up with health and environmental issues. Cycling is a sport recommended for the aged, osteoporotic patients, and overweighted people. It helps to strengthen the muscular power of the lower body and the cardiopulmonary function, and maintain and improve physical strength [1]. However, if improper pedaling postures and pedaling loads are used, the possibility of injury increases. Accordingly, many studies have been conducted on the cycle fitting to determine the proper pedaling postures. For example, Bae [2] developed a riding machine that can automatically control the frame size for proper pedaling posture. Umberger Y.H. Shin, J.S. Choi, D.W. Kang J.W. Seo, J.H. Lee, J.Y. Kim, D.H. Kim, and S.T. Yang are with the Department of Biomedical Engineering, College of Biomedical & Health Science, Konkuk University, Chungju, South Korea (e-mail: syhjj1004, jschoi98, dwkang00, jwseo0908, joohack12, sugicube, dehyeok.kim, hilton98@gmail.com) G. R. Tack is with the Department of Biomedical Engineering, Research Institute of Biomedical Engineering, College of Biomedical & Health Science, Konkuk University, Chungju, South Korea (Corresponding Author e-mail: grtack@kku.ac.kr). [3] compared the pedaling forces at four different seat tube angles (69, 76, 83, and 90°) and showed that the pedaling force increases as the angle decreases. Bini [4] proved that knee injury risk in pedaling can be decreased by adjusting the saddle height to achieve the knee angle of 25-30°. Choi [5] showed that an increased saddle height directly affects the range of motion of the joint and the muscle length. Macdermid & Edward [6] reported that the 170mm pedal arm produces higher peak power and reaches the peak power faster than 172.5 and 175mm pedal arms. According to such studies, the variables that can cause injuries and exercise effects during pedaling are diverse such as the seat tube angle, saddle height, and pedal arm length. To investigate the influence of all such variables, repeated experiments that control each variable are needed, and it is also difficult to obtain consistent test results. The variables to be controlled by musculoskeletal model can be done more easily than by actual experiment. Musculoskeletal model has several advantages. For example, though the numbers of electromyography (EMG) channels are limited, a musculoskeletal model can identify all muscle activities. Due to such merit, studies have been conducted on finger muscles and tendons [7] and the muscle force after lower limb treatments [8]. There are mainly two kinds of musculoskeletal models. One is the forward dynamics model, in which the subject’s physical segment length and experimental condition are directly used. The other is the inverse dynamics model, which uses the subject’s physical segment length, motion, and pedal forces obtained through experiment. The inverse dynamics calculates several results by using the forces applied to the pedal, so more precise and diverse results can be estimated, but it is disadvantageous since it requires actual experiments. Therefore, in prior cycle fitting estimations, the forward dynamics is considered more effective than the inverse dynamics since diverse variables can be controlled and applied through simple anthropometric measurement without actual pedaling experiments. The forward dynamics of a musculoskeletal model is based on its physical size and the kinematical condition. It analyzes muscle powers and joint moments numerically due to various movements. Furthermore, if it is applied to cycling, simulations are possible by controlling the body height, weight, and length of each segment of the subject, as well as the cycling variables to be fit. That is, if an optimal model applicable to cycle fitting is established, the actual experiments can be minimized. There have been many studies on such musculoskeletal models applied to cycling. Jeffery [9] investigated the crank power and the timing of muscle activities depending on the A Study on Human Musculoskeletal Model for Cycle Fitting: Comparison with EMG Yoon- Ho Shin, Jin-Seung Choi, Dong-Won Kang, Jeong-Woo Seo, Joo-Hack Lee, Ju-Young Kim, Dae-Hyeok Kim, Seung-Tae Yang, Gye-Rae Tack T World Academy of Science, Engineering and Technology International Journal of Biomedical and Biological Engineering Vol:9, No:2, 2015 92 International Scholarly and Scientific Research & Innovation 9(2) 2015 scholar.waset.org/1307-6892/10000324 International Science Index, Biomedical and Biological Engineering Vol:9, No:2, 2015 waset.org/Publication/10000324