Biomechanical Analysis of Athletes Sprinting With Varying Degrees of Resistance

Michaela Ott Author

04/03/2021 Added

53 Plays

Description

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

Searchable Transcript

Search:

[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.

The screen size you are trying to search captions on is too small!

You can always jump over to MediaHub and check it out there.

Comments

0 Comments

Biomechanical Analysis of Athletes Sprinting With Varying Degrees of Resistance

Description

Searchable Transcript

Comments icon comment

Related Channels

Comments