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Relative timing - Lab Report Example

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In understanding how generalized motor programs are characterized in memory, physicist took the step to know which features of movement are constant or invariant in the process of trial with different surfaces. …
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Relative timing
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Relative timing Laboratory Report Introduction In understanding how generalized motor programs are characterized in memory, physicist took the step to know which features of movement are constant or invariant in the process of trial with different surfaces. Generalized motor programs have unique timing structure which forms fundamental patterns that will help the experimenter identify invariant features (Schmidt & Wrisberg, 2008). On the other hand, the relative timing of a movement pattern represents the signature feature that helps the experimenter be able to clearly differentiate the phases.

By comparing the movements of a set of ratios for a limb in action, that is the right and left leg, thee experimenter achieve a more precise method used in viewing relative timing and determining temporal structures of the right and left foot in action. Statement of Purpose To define the relative timing trend when changing from one class of motion to another, in the case of cycling movements of the right and left foot, and also involve the student in calculating the sets of ratios for relative timing.

Hypothesis Phase ratios will be constant for each trial. Considering the invariant characteristics of the respective movement, interchanging pattern of gait, leads to the two cycles overlapping eventually, hence the assumption of a constant proportion within each category of gait. Method The relative time for the cycle events are recorded for each trial for the right foot. These include Toe-off, Midswing, Heel Strike and Midstance. These numerical data are entered into excel. The same is procedure is done for the left foot.

Using the provided formula for calculation, phase durations and phase ratios are calculated using excel formula of subtraction and division respectively. Using the insert graph toolbar functions, the graph of phase ratios is derived. Results The results are presented and graphed below. Relative Sheet Timing Data Right Foot   Trial 1 Trial 2 Trial 3 Trial 4 Cycle Events Toe-off 0.2 0.18 0.2 0.22 Midswing 0.38 0.36 0.34 0.37 Heel Strike 0.62 0.6 0.62 0.6 Midstance 0.74 0.71 0.73 0.74 Toe-off 0.93 0.88 0.93 0.9 Duration 0.73 0.7 0.73 0.68 Phase Duration F 0.18 0.18 0.14 0.15 E1 0.24 0.24 0.28 0.23 E2 0.12 0.11 0.11 0.14 E3 0.19 0.17 0.20 0.16 Phase Ratios F 0.25 0.26 0.19 0.22 E1 0.33 0.34 0.38 0.34 E2 0.16 0.16 0.15 0.21 E3 0.26 0.24 0.27 0.24 Right Foot graph Left Foot   Trial 1 Trial 2 Trial 3 Trial 4 Cycle Events Heel Strike 0.26 0.23 0.24 0.27 Midstance 0.37 0.36 0.37 0.38 Toe-off 0.56 0.54 0.56 0.56 Midswing 0.75 0.7 0.74 0.74 Heel Strike 0.98 0.95 0.98 0.97 Duration 0.72 0.72 0.74 0.7 Phase Duration E2 0.11 0.13 0.13 0.11 E3 0.19 0.18 0.19 0.18 F 0.19 0.16 0.18 0.18 E1 0.23 0.25 0.24 0.23 Phase Ratios E2 0.15 0.18 0.18 0.16 E3 0.26 0.25 0.26 0.26 F 0.26 0.22 0.24 0.26 E1 0.32 0.35 0.32 0.33 Left Foot graph Discussion The data presented above show set of calculated ratios that define the relative durations for the step cycle of four phases.

Obviously on the treadmill, the experimenter starts by walking before increasing their pace to a running state. Notably, the relative timing trend of the four stages remains the same for the left foot (indicated by the closely flat gradient of the lines). A credible interpretation of the result above would be that performers apply variable energy to their right foot when using the same generalized motor program. The relative time for a running program substantially differs from a walking program.

References Fagard, J., & Wolff, P. H. (1991). The Development of timing control and temporal organization in coordinated action : invariant relative timing, rhythms, and coordination. Amsterdam: North-Holland. Schmidt, R. A., & Wrisberg, C. A. (2008). Motor learning and performance : a situation-based learning approach. Champaign: Human Kinetics. Tan, H. (2012). Technology for education and learning. New York: Springer.

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