Variation of biomechanical data
Did you know that: The highest consistency of movements was found to be quite typical for stroke seats in eights and could be related to the specifics of stroke rowers and their ability to set a consistent rhythm for the crew.
The main method of BioRow data analysis (RBN 2017/02) involves deriving arrays of average values for each variable (angles, forces, seat position, etc.) over the sample period. These arrays represent typical patterns (curves) of rowing technique and allow reliable comparisons of various samples (different stroke rates, rowers, crews, boat types, etc.). Additionally, BioRow software allows for deriving arrays of standard deviations (SD) for each variable, representing data variation in each data point between stroke cycles.
Here, we present examples of SD patterns for four main variables measured with the BioRow system: horizontal and vertical oar angles, handle force, and seat position. Horizontal oar angles had the highest SD during the second half of the drive, while the SD of vertical angles peaked after the catch. Handle force SD exhibited two peaks: one at the beginning and another at the end of the drive. The SD of seat positions also had two peaks, occurring before and after the catch.
The variation in biomechanical data could be related to the psychological characteristics of each rower and their motor control.
To analyze variation data statistically, each pattern was split into seven sections during the stroke cycle, and average SD values were derived for each of these sections:
1. End recovery (from the cycle start to the last point before the catch);
2. Catch (three data points around it);
3-5. Drive time was split into three equal sections: 3. Start, 4. Middle, and 5. End;
6. Finish (three data points around it);
7. Start recovery (to the cycle end).
Additionally, average SD values over the stroke cycle were derived. Statistical data can be presented as averaged patterns, resembling the measured ones.
SD values are obviously related to their magnitude and the rate of change of each variable—the higher the value and the faster it changes, the higher the variation at that data point. This reflects the specifics of the method. Normalizing SD using its ratio to the magnitude or rate of change is not possible because of errors at zero points of the divider.
It was found that the larger the boat, the higher the variation. The most variable force curve was found in eights, while the most consistent was in singles. Oar angles and seat movements were also more variable in eights. These findings were quite surprising and were not method-related, as the magnitude of forces is usually higher in small boats. The reason for this phenomenon could be the different experience levels of the rowers: most of the measured population in eights were juniors, while more elite rowers were tested in small boats.
The variation of all variables had the lowest values at the finish of the drive, which means this is the most consistent part of the cycle.
Comparing SD curves at various stroke rates, the following practical conclusion can be made: the lowest data variation was found at medium stroke rates (28 - 33 spm), and the highest variation was at the lowest and highest stroke rates. The reasons for this finding are not entirely clear yet.
The information on the variation of biomechanical data can be utilized for practical evaluation of rowers’ techniques: for example, for selecting the most consistent rower in a crew for the stroke position, for analyzing a rower’s performance in various crew combinations, at different stroke rates, under fatigue, and in competition stress, etc.
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©2024 Dr. Valery Kleshnev