Ciba Foundation Symposium 82 - Human Muscle Fatigue: Physiological Mechanisms
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The 1 st , 15 th and 30 th min of running are shown in the left, middle and right columns, respectively. Table 3 summarizes the regression results for the cases displayed in Figure 7. The results show the following: a For k 1 in time zone 1, the Pearson correlation r p starts off by a low value of 0. This correlation, however, was lower in the 15 th min 0.
But here too this correlation was decreased with the development of fatigue, suggesting that higher peak acceleration at the tibial tuberosity is associated with a high k 3 parameter value in time zone 2.
Simple linear-regression made to express the relationship between the tibial tuberosity peak acceleration and parameter values results of 10 subjects. The regression was performed for the 1st min leftmost panels , the 15th min middle panels and for the 30th min rightmost panels.
It has been shown that in running with shoes, the foot is restricted from bulging sideways, thus limiting the vertical deformation to an average of 5. This explains the higher stiffness during the first time zone compared to bare foot running. It also explains the lower stiffness during the second time zone compared to bare foot running. It has also been shown by that better energy absorption and impact shock attenuation is associated with lower stiffness [ 51 ]. The correlation found between low stiffness in the first time zone to the high stiffness in the second time zone is obvious from the anatomy of the heel pad, which consists of nearly closed collagen cells filled with fatty cells [ 27 , 48 ].
The vertical orientations of these cells, together with the high viscosity of the fat tissue are the major factors responsible for the absorption of the impact energy at heel strike. Initially, the fat flows sideways and small loads result in high deformation low stiffness.
Mechanisms of Human Muscle Fatigue | SpringerLink
In the second time zone, after the heel pad has already considerably deformed, further increase in deformation provokes a high load, thus high stiffness. The effect of fatigue could be explained by means of the heating effect during the course of running. With nearly 80 heel-strikes per min, the whole running duration of 30 min results in some heel-strikes, each of which causing a rapid deformation of the heel pad and during which the fatty tissue frictions while squeezed out of the collagen cells.
The accumulated heat due to friction reduces the viscosity and the vertical displacement is accelerated, causing a reduction in stiffness during the first zone of heel strike. In the second zone, however, the thinner remaining tissue together with the underlying bone evokes an increase in stiffness. In the present study the average running distance per test was 6. We have measured and analyzed the following: respiratory data to monitor global fatigue; and accelerometry, to provide quantitative information on loading of the major segments of the lower limb.
While providing accelerometer boundary values for the model system, accelerometry is an advantageous method due to its being non-invasive. We have addressed a major fatigue-related factor taking part in exposing the shank to stress fractures risk: the decline in end tidal carbon dioxide pressure, the latter expressing metabolic fatigue [ 31 , 58 ]. The mechanical consequence of fatigue in long-distance running is two-fold: enhanced impact acceleration due to global fatigue and muscle activity imbalance due to local fatigue before and during foot contact, resulting in the development of excessive shank-bone bending stresses and higher risk of stress injury [ 11 ].
While departing from the stiffness constancy concept, the model revealed that a correct and sufficient variability of the joint stiffness is a two-region piece-wise constant stiffness indicating that a higher order of non-linearity is not necessary. Joint stiffness is dominated by muscular activation [ 59 - 60 ] and as the joints stiffen, they undergo smaller angular displacements during the ground-contact phase, also resulting in smaller excursion of the hip and higher leg stiffness.
The fact that the simple model of a piece-wise constant stiffness can predict major features of the running exercise makes it an effective tool for future designing of artificial legs and robots and also for the development of more accurate control strategies.
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Yacov Ben-Haim. Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3. Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. Edited by Tarun Goswami. We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists.
Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Mizrahi and D. Downloaded: Introduction The human motor system benefits from remarkable muscular redundancies: A motor task is normally performed with the simultaneous involvement of more muscles than strictly necessary.
Biomechanical modeling of the lower limb This section deals with the modeling of the heel-strike event.
Model equations For the above model, the force diagram, as presented in Figure 2 , yields the following model equations. Experimental setup Information about the impulsive loading along the skeletal elements in long-distance running can be non-invasively obtained from the foot-ground reactive forces [ 25 ] and, more directly, by measuring the transient accelerations on the shank caused by impact. Impact accelerations Non-invasive in vivo measurements of acceleration and impact transmission along the human body were made by externally attaching light-weight, high-sensitivity accelerometers at strategic points including bony prominences, such as the tibial tuberosity below the knee area, the greater trochanter near hip level and the sacrum area at the lower back [ 13 , 15 , 22 , 26 - 29 ].
Running fatigue tests An overview of the experimental setup is shown in Figure 3. Parameter estimation of the model The mechanical properties of biological material are, in general, multiple variable-dependent. Model results and model sensitivity analysis Table 1 shows the sample results of the stiffness and damping parameters for the two time zones. Table 1. Table 2. Table 3. More Print chapter. How to cite and reference Link to this chapter Copy to clipboard. Cite this chapter Copy to clipboard J.
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Mechanisms of Human Muscle Fatigue
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