Gait Analysis
Part 1: Introduction to Gait Analysis:
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?
Some engineers study the way people walk! Gait analysis is the 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.
What I have just described is a good example of what we call engineering analysis—the application of scientific and analytic principles to reveal the state and properties of a system. Doing this helps engineers begin to understand how and why systems behave the way they do.
For the gait analysis lesson we are beginning today:
→ You will perform engineering analysis on the gait data from several human subjects to understand what properties of a walking gait could be utilized to identify different types of people and what possible measures related to the human gait could be utilized to quantify that gait.
You will use accelerometers to collect and graph acceleration vs. time data that can help in gait analysis—all part of practicing the engineering data analysis process.
In today's activity, several human subjects of different physical characteristics will walk across the room. The reason for the diversity is that walking gait characteristics can vary between subjects based on factors such as age, health and physical activity.
As you observe the individuals' different gaits, keep the following four questions in mind and attempt to pose answers:
Questions to consider
How would you describe the walking gait of each subject?
What quantities could be measured to analyze walking gaits?
In the context of walking gaits, what do the terms symmetry, variability and dynamicity mean?
How could an accelerometer be used to quantify walking gaits?
Vocabulary/Definitions
accelerometer: (Links to an external site.) A device that measures the physical acceleration experienced by an object.
dynamicity: (Links to an external site.) In terms of gait analysis, the quantification of variations in kinematic or kinetic parameters within a step.
gait: (Links to an external site.)The stride of a human as s/he moves his/her limbs.
metric: (Links to an external site.)A quantitative indicator of a characteristic or attribute.
model: (Links to an external site.) 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: (Links to an external site.) In terms of gait analysis, the quantification of differences between left-foot and right-foot steps.
variability: (Links to an external site.) In terms of gait analysis, the quantification of fluctuations from one stride to the next.
Adult vs Children given data, practice To Do:
1. Click here to download the Data File and add it to your Drive. (Links to an external site.)
2. Open in Google Sheets (Or Excel)
3. Go to the spreadsheet tab titled "Adults & Children."
4. Identify metrics that could be used to distinguish between adults and children.
5. Using one or more of the metrics, construct a predictive model for categorizing an unknown subject as an adult or a child.
6. Test your model: Open the spreadsheet tab titled "Unknown Subjects" and access GSM data for unknown subjects.
7. Apply your model to predict whether the unknown subjects are adults or children. By doing this, you are attempting to assess the reliability of your model and the limitations that introduce uncertainty to your predictions.
8. Who is whom? Submit a paragraph (Google classroom) that outlines who do you think is an adult and who is a child include your reasoning using your data to support your ideas.
Materials List
Each group needs:
A cell-phone (preferably Android)
An accelerometer ("Physics Toolbox Accelerometer" by Vieyra Software or alternative software/app)
Duct tape, cord, belt, strap, or some way to attach a cellphone to a person's lower back or other parts as needed.
Google Sheets or Microsoft Excel®, or similar spreadsheet program
Part 2: Activity:
Important!!! Before you start this activity, Discuss with your group and make a detailed plan on your Engineering Journal. Revise/update the plan as you proceed with the project.
►Install the "Physics Toolbox Accelerometer" by Vieyra Software on your phone (free Version)
Experiment with your phone to identify the axis
Make sure that when placing the phone on your body you are recording the right axis.
Orient the phone with the recording axis horizontal and parallel to the direction being walked.
Practice saving your data to a .CSV file and importing it to Google docs (See video tutorials below).
Measurement: Gait Patterns
QUESTION:
What is the relationship between the HEIGHT and GAIT FREQUENCY for walking humans?
Try This:
Attach a mobile device to your lower back or chest area and collect data while walking using the App.
For various subjects, compare, gait, symmetry, variability and dynamicity.
Compare these traits in the data to physical characteristics, such as height or leg length.
if you want record video to compare data to visual observations.
Record everything in your notebooks! For EACH Trial include:
Are you running or walking?
How you are holding the phone.
Step Count
How many seconds of data you are collecting
Which foot you start with
Make sure you pause for a second or two before you begin data collection.
Save your data to a .CSV file and importing it to Google docs after each collection, clearly label the data collection by indicating the file name in the notebook.
How to Import .csv Files to Google Spreadsheet
A Nice Graph of Walking Data:
Goal: Get a graph that looks as nice as this one. Take a screenshot of your own and share on google docs (make sure you give me the ability to see it.)
The graph below shows an example acceleration vs. time graph. Anterior acceleration data collected by an accelerometer worn on the lower trunk of a walking person.
Analyzing acceleration vs. time graphs such as the one shown in the graph above reveals the repetitive nature of the gait data, making it evident that metrics can be derived from the data. The figure below provides an example of how features of the acceleration vs. time graph corresponded to step characteristics.
Part 3: Data Analysis
Introduction
In this activity, you will be constructing a model that could be used to represent gaits of different individuals of different groups (i.e. age groups, gender groups etc...), from data that you collected. Engineers spend a lot of time observing systems and developing models that attempt to predict how a system behaves. After performing this type of analysis, engineers have a better understanding of the system, its underlying constraints, and where it can be improved.
Summary
In this open-ended, hands-on activity that provides practice in engineering data analysis, you are given gait signature metric (GSM) data for known people types (adults and children) and will collect data on people in the class. Working in teams,you will analyze the data given and collected by you and develop models that you believe represent the data. You will test your models against similar, but unknown data to see how accurate your models for example in predicting adult vs. child human subjects given known GSM data. you will manipulate and graph data in Google Sheets or Excel® to conduct your analyses.
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.
Data Collection—The process starts with data collection using any number of strategies. Data collection might take the form of an experiment in which you conduct trials to measure the effect of one variable on another by controlling all other possible variables. The collection might be a survey of something by sampling to gather information. It is important that the survey be unbiased, random and representative of the group you are sampling. Data may already exist, eliminating the need to collect something new. In the business world, it might be historic sales, production or costs. In academia, it might be test scores. In engineering, data is collected on production processes, historical usage or environmental factors, and stress or strength measurements. Data is everywhere and often the challenge is not so much finding data as limiting it to what is relevant to study.
Data Analysis—The analysis of the collected data is a critical step. To do this, you carefully look at the collected data in an organized way, such as a table, spreadsheet or other computer application. You probably want to graph the data because trends are easier to notice in graphical format. This step is really about identifying patterns that might be present. It is possible that no strong trends are present in the data. Finding no trend is not necessarily bad; it just tells you that the variables are not related.
Mathematical Modeling—Modeling the data that was collected and analyzed is where mathematics occurs in this process. You might use a graphing calculator, computer spreadsheet or other software application to generate an equation that represents the data. These uses of technology also provide statistical measurements such as variance and correlation that can help you understand the effectiveness of your equation (model).
Reporting—The final step in this process is to report the data and model that represent it and then make predictions using the model to support decisions. If you have a model that statistically represents the data accurately, it should be possible to make fairly reliable predictions. You can present the results in tabular or graphical form, or a combination of both. Indicate your prediction by showing an extrapolation using your model and present that information as support for a decision. Be aware that any predictions made are only that—predictions. If the trend changes, your prediction will not be correct. The process of data analysis is a tool to make an educated guess about the future and not a guarantee that predictions come true.
Analyzing Data with Google Spreadsheet
Step 4: Mathematical Model (Predictive Model)
Analyzing your data, try to correlate one of the gait characteristics you have measured through the app with one or more Human-Parameters of your choice (Height, legs length, weight, gender age etc...). The more subject you measure the better!!! A Model is an equation that can be utilized to predict your chosen Human-Parameters. See also Step 3 (Hint: once you have decided which correlation to investigate Find a regression equation using Google sheets or Desmos)
Linear regression on Google sheet
Waves review:
f = 1 / T , f = c / λ
Formulas and equations for frequency and wavelength
The formula for frequency is: f (frequency) = 1 / T (period).
f = c / λ = wave speed c (m/s) / wavelength λ (m).
The formula for time is: T (period) = 1 / f (frequency).
The formula for wavelength is: λ (m) = c / f
λ = c / f = wave speed c (m/s) / frequency f (Hz).
The unit hertz (Hz) was once called cps = cycles per second.
c = λ × f λ = c / f = c × T f = c / λ
Report Requirements and Guidelines:
How2WriteUpALab4ENG.pdf