Concussions

pdf

School

Temple University *

*We aren’t endorsed by this school

Course

1061

Subject

Mechanical Engineering

Date

Apr 3, 2024

Type

pdf

Pages

Uploaded by DeaconSharkMaster285

T E M P L E U N I V E R S I T Y P H Y S I C S 1 10/4/2023 10:09 AM Concussions Recent studies on traumatic brain injuries have shown that even relatively minor head impacts can lead to severe effects on neurological function later in life. Accidents and sports are two of the leading causes of these injuries. In some studies, researchers placed accelerometers on human subjects experiencing real collisions and evaluated the players afterward to look for concussion-causing acceleration values. A critical factor to whether injury occurs is the time interval over which the force and acceleration occur, so impulse is a useful metric for this research. Not surprisingly, the impulse can also be written in terms of the momentum change of the colliding objects. J = D p = m D v = F D t In this lab we’ll first measure the forces on a model head with and without a helmet, then we’ll go on to analyze some published data in this field of study. Learning goals for this lab: • Extend physical analysis of collisions to a real-world problem. • Compare collision data with and without a helmet. • Practice interpreting published scientific study on predicting concussions based on acceleration levels. Apparatus: computer with PASCO interface and Capstone software, cart and track, Pasco force sensor Part I. Head impact with no helmet The “head” in this experiment will be the sensing element on the force sensor. We will collide a cart into the head to simulate the type of collision a football player may experience in a head-on collision. 1. The force sensor should be mounted to one end of the track and the other end should be slightly elevated. Plug the force sensor into the Pasco interface in Capstone check that the sensor is detected in the Hardware Setup menu. If your sensor is not automatically detected, click on the input you are using and identify your model of force sensor from the list. Zero the force sensor by pushing the tare button on the sensor itself. The smart cart does not need to be connected to Capstone since we are only measuring force in this version of the experiment. 2. Set the force sensor’s sample rate to at least 2 kHz (else you won’t have a fast enough sample rate to get detailed force vs. time data). 3. Make a graph display of force vs. time. 4. Remove the helmet from the head. Select a release location for the cart a few cm from the head, we will use this same release point for every trial. Click record and release the cart from rest, allowing it to roll down the track and collide with the head. Check your data by zooming into the x-axis at the time of the collision (zoom into the x-axis by hovering over it and scrolling, when done correctly the y-axis is not affected). Your collision should produce a smooth peak with a maximum force comfortably below the 50 N maximum of

2 these sensors. If the peak is jagged or the force is near 50 N, try repeating with a gentler collision by making your track less steep or starting closer to the head. Once you have a smooth collision recorded, repeat data collection at least twice more, releasing from the same point each time. 5. For each run find the impulse by highlighting the collision region in your force vs. time plot and clicking the integrate button to find the area under the curve (the impulse). Increase the decimal places of the dispalyed area value by right-clicking on it, clicking tool properties then numerical format, and increasing the decimal places. Also use the coordinates delta tool to find the maximum force and the duration of each collision. Put your data in a table in Excel like the one below. 6. Now put one of the soft rubber helmets on the force sensor and redo the collision three more times so we can compare the data with the helmet to that without the helmet. Make sure to release the cart from the same height as before. Add a row in your Excel table for the average of the values from the three trials. Impulse (Ns) Fmax (N) D t (s) Without helmet Trial 1 Trial 2 Trial 3 Average With helmet Trial 1 Trial 2 Trial 3 Average Question 1. What quantity is represented by the area under the curve in a graph of force vs. time? Question 2. How does the duration (i.e., the time scale) of the impact differ between the two cases (with vs without the helmet)? Question 3. Compare the average impulse from the trials with the helmet to those without the helmet. Was there a significant difference (more than about 20 %) in the average impulse between the two conditions? Was there a significant difference in the average maximum force between the two? It has been shown that higher accelerations of the head cause concussions, does your data support the common assertion that helmets help prevent concussions? Support your answer. Part II. Further analysis Figure 1 below is published data from a study predicting the likelihood of concussion based on the linear and rotational acceleration experienced in a collision. The evidence shows that both rotational and linear acceleration contribute to concussion and a combination of the two is more likely to cause a concussion. In other words, a violent turn of the head can cause a concussion, especially if linear acceleration is also involved. The plot accounts for both types of acceleration by showing the percent chance of a concussion given a combination of linear and rotational acceleration values.

3 Question 4. When the person experiences a rotational acceleration of 7000 rad/s 2 , what value of linear acceleration in g’s would result in a 5 % chance of concussion? What linear acceleration causes a 5% chance of concussion when the rotational value is lowered slightly to 6000 rad/s 2 ? Question 5. Calculate the linear acceleration of the cart from Part I using the trial with the highest force value you recorded. To do this, assume the cart has a mass of 0.2 kg and use Newton’s 2 nd Law to find the acceleration. Then Using this value of linear acceleration in g’s refer to Figure 1 to determine the rotational acceleration that would have to occur for a 1% chance of concussion in your experiment. Figure 1. Family of curves showing chance of concussion as a function when combining linear and rotational acceleration. Data modified from Rowson, et al., Annals of Biomedical Engr 2013. Figure 2. Family of curves showing chance of concussion when combining linear and rotational acceleration. Data modified from Rowson, et al., Annals of Biomedical Engr 2013.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

4 Question 6. Figure 2 is a graph of the empirical cumulative distribution function (CDF) vs the linear acceleration for two different data sets (HITS and NFL). The CDF is the fraction of observations that are below the specified value on the x axis. For example, about 90 percent of the sub-concussive impacts in the HITS data (solid black line) occurred at accelerations below 50 g. According to the NFL data set, what was the highest acceleration experienced that did not cause a concussion? What was the minimum acceleration that did cause a concussion?