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Locomotor Neuromechanics — Term Project A: (35 marks total)
Objectives: You will design and present a research proposal to test a locomotor hypothesis of
your choice. To successfully complete this project, you will be asked to develop a research
question and hypothesis, evaluate relevant literature, design a proposed protocol and present a
final proposal. This is an opportunity for you to explore a topic of interest in greater depth, as well
as integrate and apply the concepts you learned in class. In addition, you will work on your oral
and written communication skills.
Your research question and proposal should involve something you can easily and safely pilot on
yourself, using accelerometry data collected from your phone during gait (see below). Do not
pilot your proposed experiment on other people. It is okay if in your proposal you design
something more complex, or that would need to be tested on a specific clinical population, but
then explain that in piloting you will do a partial or ‘mock’ experiment. You are encouraged to use
office hours to discuss and get feedback on proposal ideas from myself and your TA. Questions
specific to coding and Matlab should be directed to Anthony. There will be three parts to your
team project:
Part A – Gathering gait data
We are living through an unprecedented change in how human movement can be monitored,
managed, and understood. Mobile health technologies, operating on smartphones and other
wearable devices, are being used by millions of people around the world to track their activity—
be it steps, or heart rate, or other metrics—as they go about their daily lives. Here, you will use
your phone to capture accelerometry data during gait and start to brainstorm about how you might
use data like this to test a research question of interest.
Your tasks are as follow:
1. Read Benson et al. (2018) (in Course Readings) to get an idea of the types of research
questions wearable devices are used to answer and the metrics typically employed.
2. Download MATLAB Mobile App (MathWorks). This app is free with your PC installation
of MATLAB using your Queen’s email. The Mobile App is available for both iPhones and
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Android. This application allows us to collect from the 6-DOF inertial measurement unit
(IMU) embedded within your phone. If you do not have access to a smartphone, or a
smartphone that is compatible with this app, this is not a problem. Please contact your TA
and we will help find a solution to complete all parts of the Term Project.
3. Collect an axis orientation trial. The purpose of this trial is to determine how the IMU is
orientated with respect to your phone’s orientation. As such, we are looking for the
directions of the IMU’s ‘x’, ‘y’ and ‘z’ acceleration directions. Hold your phone upright in
front of you, with the screen facing to your right, the long axis of the phone perpendicular
to the ground (you are replicating the orientation of the phone when it will be mounted to
lateral side of your leg, see below). Start collection and hold the phone steady for 5
seconds. Rotate the phone 90 degrees such that the long axis is now parallel to the
ground. Hold the phone steady for 5 seconds. Lastly, rotate the phone another 90 degrees
such that the screen is facing upwards (and the phone is flat). Hold the phone steady for
5 seconds. Stop recording and name your file Orientation Trial 1.
In a separate recording, hold your phone upright with the long axis perpendicular to the
ground again. Then, rapidly accelerate your phone forward and then slowly bring it back
to the starting position. Hold the phone steady for 5 seconds. Rapidly accelerate your
phone to the right, then slowly bring it back to the starting position. Hold the phone steady
for 5 seconds. Rapidly accelerate your phone upward, then slowly bring it back to the
starting position. Hold the phone steady for 5 seconds. Stop recording and name your file
Orientation Trial 2.
4. Mount your phone securely to the lateral side of your right leg, with the screen facing
outward and the long axis of the phone perpendicular to the ground when you are
standing. Make sure you can still start and stop collection.
5. Collect three trials of gait data. Mark on the floor, or ground, a straight-line distance of
roughly 10 meters (if you do not have this distance of space available, please feel free to
adapt this distance to whatever space you have). Begin at the starting line at a standstill,
start collection on the app, and remain standing still for roughly 10 seconds. Walk at a
comfortable pace to the finish line and then again standstill for roughly 10 seconds. Stop
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collection on the app. Repeat this collection for two additional trials, one at a slow speed
and one at a fast speed.
6. Load the files into MATLAB. Note that your files are saved as a ‘.mat’ format locally on
your phone. If enabled, your data is also stored online on MATLAB Drive. Once you
downloaded the files and have them in your working directory, drag the file onto your
workspace or use the load function.
7. Plot your raw orientation data. Generate a figure with four subplots. Each subplot would
be a plot of Acceleration (ms2) vs. Time (s). For the first subplot, plot the ‘x’, ‘y’, and ‘z’
raw acceleration data from Orientation Trial 1. For the second subplot plot the same raw
acceleration data collected from Orientation Trial 2. For the third and fourth subplot, plot
the high pass filtered acceleration data collected from Orientation Trial 1 and Orientation
Trial 2, respectively. Use the butter function to generate a Butterworth filter. The function
takes in three inputs: 1) the filter order which can be 1 in our case. 2) the cut-off frequency
denoting what ‘frequency’ do we want to start attenuating. Remember that you have to
divide the cut-off frequency by half of the sample rate [ e.g., if you sampled at 100Hz, then
the cut-off input to the function should be 0.1 / (100/2) ]. Try values from 0.001, 0.01, 0.1,
and 1. What do you notice as high-pass cut-off frequency increases. The third input should
be the filter type. In class we created a low-pass filter (we want ‘pass’ through low
frequencies and attenuate high frequencies). Here we are creating a high-pass filter doing
the opposite. As such, the third input should be ‘high’ – include quotations. The function
generates two variables representing the polynomial coefficients necessary to generate
the filter. In all the function should be written in this format:
[b,a] = butter(1, 0.1 / (100/2), ‘high’);
We then apply the Butterworth filter using the filtfilt function. This function takes in the ‘b’
and ‘a’ parameter and the signal we want to filter. The function outputs the filtered version
of our input signal (denoted to be “K”). In all the function should be written in this format
(assuming my acceleration data = K)
filtered_data = filtfilt(b, a, K);
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Plot all three accelerometer axes (each in a different color) and use a legend. (Tip: useful
MATLAB functions: plot, subplot, legend) (4 marks)
8. Plot your walking data. Generate an additional figure with three subplots, each plotting
your high pass filtered walking acceleration data (each in different colors) with legend.
Don’t forget to high pass filter your data walking data. (3 marks)
9. Determine accelerometer axis orientations. By inspecting data in the orientation trials,
determine which accelerometer axis (x, y, z) is vertical, anteroposterior, and mediolateral,
and which acceleration direction is positive (up or down, forward or backward, right or left).
Draw a sketch of your accelerator mounted to your leg and indicate on the sketch the
positive x, y, and z axes. (4 marks)
10. Determine gait acceleration orientations. Analyze data in the comfortable walking trial
to determine which orientation/axis you find the greatest and lowest magnitude of
accelerations? Explain how you determined this. Intuitively, does what you found make
sense when you consider the mechanics of gait? Explain. Referencing your plotted gait
accelerations may help illustrate your answer. (4 marks)
11. Solve for the resultant acceleration for each walking trial vs. time. Resultant
acceleration can be used to capture the ‘total’ or ‘net acceleration’ and is calculated as:
a
r = √(x2 + y2 + z2)
12. Determine resultant acceleration average magnitude. Determine which walking trial
has the greatest and lowest magnitude of resultant accelerations? Explain how you
determined this. Intuitively, does what you found make sense when you consider the
mechanics of gait? Explain. A bar plot would help illustrate your answer. (11 & 12, 4 marks)
13. Determine step rate. Write a script to automatically determine the average step rate
during each walking trial. Produce plots of resultant acceleration vs. time for each trial that
includes a marker showing where your code identifies each step. (Tip: useful MATLAB
functions: butter and filtfilt, islocalmax or islocalmin). Visually if your code determined step
rates that seem reasonable. (7 marks)
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14. Calculate an estimate of the length of your steps during each trial. Explain how you
calculated this. Did your step rate increase or decrease as you increased your walking
speed? Explain why this might be. (3 marks)
15. Calculate one additional gait metric of your choice using your data. Explain what metric
you calculated, report the value for each trial, and discuss what it might tell you. (2 marks)
16. Briefly describe a research question you are considering for your final term report. Do
so in 100 words or less (just give me the big picture idea). This will help me provide some
feedback. You are not obligated to stick with this idea for the final project if you come up
with another idea you like more. (4 marks)
Due date: March 9th, 2023 (2:30pm)
Weight: 25% of term project grade