Gait Analysis
Introduction to Gait Analysis
In order to understand the gait analysis assignment, we needed to get some background information on what gait is and how it can be a distinguishing factor. *Our final presentation and report are above*
In order to gain a greater understanding, we answered the following questions:
What is a gait?
Have you ever thought about the way you walk and run?
Or noticed that the people you know have a distinct way of walking? What differences do you see?
Can you recognize certain people by how they walk? How might differences in gaits help you identify people?
What is noticeable about how those people move?
Could you collect data on this and learn from it?
Vocabulary + Important Information
Gait analysis: systematic study of human motion, using visual observations and movement measurements for the purpose of helping people with conditions affecting their ability to walk, such as helping athletes move more efficiently and identifying posture- or movement-related problems in people with injuries. Gait analysis is important for some medical diagnostics and the area of biomechanics, so engineers design ways to test, analyze and learn from the way people walk.
accelerometer: A device that measures the physical acceleration experienced by an object.
dynamicity: In terms of gait analysis, the quantification of variations in kinematic or kinetic parameters within a step.
gait: The stride of a human as s/he moves his/her limbs.
metric: A quantitative indicator of a characteristic or attribute.
model: In technology, a description of observed or predicted behavior of some system, simplified by ignoring certain details. Models allow complex systems to be understood and their behavior predicted.
symmetry: In terms of gait analysis, the quantification of differences between left-foot and right-foot steps.
variability: In terms of gait analysis, the quantification of fluctuations from one stride to the next.
Practice
Before beginning our own gait analysis, we had to further our data analyzation skills by predicting if a subject was an adult or child based on the data provided.
Gait Analysis Report
QUESTION: What is the relationship between the HEIGHT and GAIT FREQUENCY for walking humans?
Engineering Connection
Engineering analysis is the process of collecting, analyzing, modeling data and making predictions. The reasons for this process are many, but typically the most important are:
to find useful information,
to make predictions about possible outcomes,
to support and provide evidence for the decision-making process.
Hypothesis:
A person’s height can be predicted using the mathematical equations t₊=0.227h-0.185and t₋=0.345h-0.349, given the time spent with a positive acceleration or the time spent with a negative acceleration in seconds.
Reflection
From report: Overall, this was an interesting project that further developed our understanding of data collection. We were able to learn more about our own gaits while exploring new data collection methods. Our team worked together extensively to ensure that an accurate mathematical model was produced and all aspects of the report were completed in a timely manner. We wanted to focus on how a person’s gait relates to their height.
After collecting our data, we were able to determine the the mathematical models t₊=0.227h-0.185 and t₋=0.345h-0.349 can be used to predict a person’s height, given their time spent (in seconds) with a positive acceleration or time spent with a negative acceleration. This ultimately proved our hypothesis correct, as we were able to determine these equations from the calculated averages of our time spent with positive or negative acceleration, and were supported by observations of our gaits.
Overall, our findings determine that each of us walk differently and that our individual gaits can serve as defining factors. We are different heights, so the amount of steps we take, our acceleration rates, and standard deviation of deceleration, impact our individual gaits, but can be predicted using our model.
In this project, our group worked extremely well together. This was a great test since these are the people we have selected to work with on our final CAPSTONE projects. We collaborated well by assigning individual roles using a gantt chart. Everyone communicated their ideas to each other and was receptive to feedback. We used critical thinking to determine how to approach the assignment and develop our mathematical model. Some things that we could have improved on were around how the data was analyzed and collected. We had one person primarily focus on the numbers and if given more time, it would have been beneficial to fully explain the data to every group member. That way everyone could have furthered their understanding of data analysis to the fullest extent.