Biomechanical Analysis of Athletes Sprinting With Varying Degrees of Resistance

Michaela Ott Author

04/03/2021 Added

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This project analyzed the power output, stride length, acceleration, and trunk tilt of UNL Women's Track and Field athletes when running resisted sprints.

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[00:00:01.340]Hello, my name is Michaela Ott,
[00:00:02.860]and today I will be presenting
[00:00:04.240]over my project titled Biomechanical Analysis of Athletes
[00:00:07.740]Sprinting With Varying Degrees of Resistance.
[00:00:10.950]Over this school year,
[00:00:11.860]I have had the fantastic opportunity
[00:00:13.500]to work with Dr. Kurt Tomasevicz
[00:00:15.130]in the Nebraska Athletic Performance Lab, or NAPL.
[00:00:18.470]My research project focuses
[00:00:19.890]on the biomechanical analysis
[00:00:21.460]of six UNL women's track and field athletes.
[00:00:24.470]More specifically, we analyzed the acceleration,
[00:00:27.510]stride length, trunk tilt,
[00:00:28.970]and power output of each athlete
[00:00:30.600]as they were running sprints
[00:00:31.750]while utilizing a resistance machine.
[00:00:34.550]Our ultimate goal is to provide more information
[00:00:36.990]about resistance training to their coaches,
[00:00:39.150]to aid not only in the success of the athletes
[00:00:41.810]but also in their health and safety.
[00:00:43.990]This can be achieved by minimizing the change
[00:00:46.060]in their biomechanics or running form
[00:00:47.840]when training with resistance.
[00:00:50.320]The two largest pieces of equipment used
[00:00:52.070]in this project were the 1080 Sprint machine
[00:00:54.080]and the Qualisys motion capture system.
[00:00:56.370]The 1080 Sprint machine provided the resistance
[00:00:58.560]for our athletes to run against, as well as force,
[00:01:01.230]power, and velocity over time of each sprint.
[00:01:04.190]As seen in the top image on the left,
[00:01:06.090]the resistance force was provided by a cable attached
[00:01:08.670]to a belt that the athlete wore around their waist.
[00:01:11.360]Qualisys motion capture system was able to provide us
[00:01:13.970]with the coordinates of markers that were stuck
[00:01:15.820]on specific locations of the athletes' bodies.
[00:01:18.800]Qualisys consists of multiple cameras set up
[00:01:21.230]around each participant that will be performing the task,
[00:01:24.220]as seen in the lower left picture.
[00:01:26.340]This allowed us to create 3-D models of each sprint,
[00:01:29.830]which allowed us to look at trunk tilt, stride length,
[00:01:32.360]stride frequency, and more.
[00:01:34.220]An example of the final model
[00:01:36.220]of a sprint that I created can be seen on the right.
[00:01:38.890]Each of the dots represents a sensor
[00:01:40.625]on the athlete's body
[00:01:42.000]with respect to the arbitrary origin seen in the video.
[00:01:46.020]Each of the athletes ran two 10-meter sprints
[00:01:48.530]at four different resistance levels,
[00:01:50.300]totalling eight sprints.
[00:01:51.810]The level of resistance was determined based
[00:01:53.720]on the body mass of each athlete,
[00:01:55.270]in order to maintain consistency
[00:01:57.000]in the level of difficulty of each resistance level.
[00:02:00.120]The values of the resistance load we used were zero,
[00:02:03.060]five, 10, and 15% of the athlete's full body mass.
[00:02:07.080]After each new participant warmed up,
[00:02:09.120]they began by having the sensors placed on their bodies
[00:02:11.560]so the cameras would be able to pick
[00:02:12.990]up their locations for us to create models.
[00:02:15.480]The image seen on this slide is a static model of one
[00:02:17.990]of the athletes that shows the location of each marker.
[00:02:21.020]It is accompanied by a list
[00:02:22.300]of the names of the locations to which they correspond.
[00:02:25.400]We then had them put on the belt connected
[00:02:27.270]to the 1080 Sprint machine
[00:02:28.620]and run two sprints at the same resistance level
[00:02:31.060]before applying a different load.
[00:02:33.330]We had each participant run the sprints
[00:02:35.120]at varying resistance levels
[00:02:37.240]in a different order to ensure that fatigue
[00:02:39.230]would not always be affecting the same level
[00:02:41.010]of resistance for every athlete.
[00:02:42.710]The raw data collected from the sprints was then uploaded
[00:02:45.210]to multiple systems to be analyzed.
[00:02:47.420]First, the Qualisys motion capture data was uploaded
[00:02:50.120]to their Qualisys software so
[00:02:51.610]that each marker could be labeled
[00:02:53.020]as the correct part of the body to create our models.
[00:02:56.050]Once the markers were labeled, the coordinates were exported
[00:02:59.200]to an Excel file to calculate averages
[00:03:01.780]as well as graph the results.
[00:03:03.840]The data from the 1080 Sprint machine was uploaded
[00:03:06.330]to their systems website
[00:03:07.520]and was analyzed there in conjunction with Excel.
[00:03:10.430]The slower of the two times
[00:03:11.630]for each resistance level were not taken
[00:03:13.470]into account when analyzing each variable.
[00:03:16.040]This aimed to ease calculations as well as analyze
[00:03:18.790]the more successful runs,
[00:03:20.450]considering that in track and field a faster time
[00:03:22.940]is considered better.
[00:03:24.690]The variables analyzed in this study included peak power
[00:03:27.760]on the sixth step, stride length
[00:03:29.760]of the fifth and sixth strides,
[00:03:31.720]the trunk tilt with respect to the surroundings,
[00:03:33.630]and acceleration within the first five meters of the sprint.
[00:03:36.950]First, I analyzed the power output
[00:03:38.700]on the sixth stride of the sprint.
[00:03:40.320]Values for the peak power were found using the graphs
[00:03:43.400]generated on the 1080 Sprint website, as seen on the left.
[00:03:47.190]The red arrow points to the peak power on the sixth step.
[00:03:50.420]This value for all four runs and all six athletes
[00:03:54.070]was compiled using Excel to create a comprehensive graph.
[00:03:57.610]Once in Excel, the values of each run were normalized
[00:04:00.650]by dividing the peak power by the body mass
[00:04:02.890]of the athlete, resulting in the power output
[00:04:05.290]per kilogram of body mass to remain consistent
[00:04:08.320]between athletes of different body masses.
[00:04:10.820]It was seen that there is a near linear trend
[00:04:13.010]in the power output
[00:04:14.340]of the sixth step of a resistance sprint.
[00:04:16.600]However, a percent change in the power output
[00:04:18.940]on the sixth stride does change slightly when
[00:04:21.190]adding a 5% increase in the level of resistance.
[00:04:24.410]As seen in this graph, the percent change in power output
[00:04:27.100]between different resistance levels decreases nonlinearly.
[00:04:31.160]This could suggest that there is a threshold
[00:04:33.100]of maximum power output of an athlete.
[00:04:35.278]In the future, I would like to look
[00:04:37.160]at sprints with larger resistance values
[00:04:39.160]to determine what that threshold is
[00:04:40.940]if my hypothesis is correct.
[00:04:43.410]After looking at the power output,
[00:04:45.010]the stride length was analyzed.
[00:04:47.020]The data showed that stride length decreased linearly
[00:04:49.810]with an increased resistance.
[00:04:51.800]It was seen that there was a 4.6% decrease
[00:04:54.960]in the length of the fifth stride
[00:04:56.310]and a 5.9% decrease in the length of the sixth stride
[00:04:59.780]when the resistance level was increased
[00:05:01.470]by a load of 5% of the athlete's body mass.
[00:05:04.410]Overall, the stride length of the fifth
[00:05:06.550]and sixth steps of the sprint were reduced by 5.26%
[00:05:10.630]as the level of resistance was increased by 5%
[00:05:13.700]of the athlete's body mass.
[00:05:15.390]After stride length, I looked at the trunk tilt
[00:05:17.440]of the athlete with respect to the ground.
[00:05:19.720]The angle that was measured can be seen
[00:05:21.710]in the image on the left.
[00:05:23.390]I then plotted the trunk tilt angles
[00:05:25.130]and found the slope of the best fit line,
[00:05:26.810]as can be seen in the plot on the right.
[00:05:29.010]After analysis, no conclusive correlation could be seen
[00:05:32.190]between the change in the angle of the trunk tilt
[00:05:34.360]with respect to the lab
[00:05:35.647]and the level of resistance applied to a sprinter.
[00:05:38.600]As seen in the plot on the right of the screen,
[00:05:40.430]for some athletes their running form became more horizontal
[00:05:43.410]as the level of resistance increased,
[00:05:45.410]while others became more vertical.
[00:05:47.420]The R-squared value for all data points is displayed
[00:05:50.490]on the plot and supports the inconclusive correlation.
[00:05:53.700]Finally, I analyzed the acceleration
[00:05:55.920]of each athlete at each resistance level
[00:05:58.180]through the first five meters of their sprints.
[00:06:00.550]The acceleration values were calculated using
[00:06:02.830]the 1080 Sprint machine's graphs.
[00:06:04.960]First, an average overlay was added
[00:06:06.970]in to normalize the values.
[00:06:09.320]This allowed us to have an average number
[00:06:11.410]to minimize the error that might occur
[00:06:13.280]if one athlete was in the middle of a stride
[00:06:15.810]versus one being not in the middle of a stride,
[00:06:18.350]as seen in the graph as either a peak or a trough.
[00:06:21.490]We then found an averaged value
[00:06:22.990]at five meters and divided that value
[00:06:25.270]by the time it took for the sprinter to travel
[00:06:27.420]that five meters, to find their acceleration.
[00:06:30.020]The data showed a linear decrease
[00:06:32.080]in the acceleration as the resistance levels increase.
[00:06:35.160]This can be seen in the graph on the right of the screen.
[00:06:38.550]In conclusion, we were able to demonstrate how sprinting
[00:06:41.410]under resistance will affect the biomechanics of a runner.
[00:06:44.450]Specifically, we saw that power increases,
[00:06:47.030]stride length decreases,
[00:06:48.360]and acceleration decreases as resistance increases.
[00:06:51.680]In addition, no conclusive correlation was seen
[00:06:54.140]between the trunk tilt of an athlete and the level
[00:06:56.240]of resistance with which they were running.
[00:06:58.800]In the future, we will be able to communicate back
[00:07:01.030]to the UNL women's track and field coaches to inspire them
[00:07:03.910]to utilize resistance machines
[00:07:05.509]to help train the athletes
[00:07:07.010]to be the best they can be.
[00:07:08.500]I am also planning to continue this research
[00:07:10.730]and analysis into next year.
[00:07:12.600]I will be exploring new variables as well
[00:07:14.600]as conducting statistical analysis to write a thesis.
[00:07:17.540]I'm very excited for the opportunity
[00:07:19.250]to expand on this project.
[00:07:21.040]This project was largely a team effort.
[00:07:23.280]I would like to thank the athletes for providing the data,
[00:07:25.970]their coaches for allowing them to participate in the study,
[00:07:28.930]the 1080 Sprint and Qualisys motion capture systems
[00:07:31.930]for providing me with easy-to-use technology,
[00:07:34.870]the NAPL faculty and staff for allowing me into their lab,
[00:07:38.350]and most of all, Dr. Tomasevicz for guiding me
[00:07:40.660]through the process and supporting me along the way.
[00:07:43.320]Thank you for listening,
[00:07:44.280]and here's a list of the sources
[00:07:45.870]for the images that I used in this presentation.

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Biomechanical Analysis of Athletes Sprinting With Varying Degrees of Resistance

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