Evidence of Work:

The goal of this project was to discover a relationship between an individual's gait and their height. Using that information, we then needed to make a predictive model that can estimate a subject’s unknown height when given their gait data. We collected our first set of data for seven individuals using an accelerometer. The data we got from that is the most accurate and not effective for making a prediction model. However, when putting the data in graph form, we were able to identify how specific gait characteristics can impact the results the accelerometer will show. For example, females have sharper hip movements then males, which tends to give them a bigger range of gFx and gFy. Our next attempt to make a prediction model was by using test subjects data from step count to reach 15ft and step length. Then dividing step length by step count to reach 15ft. By making the data all in similar ratios we were able to create a moderately accurate predictive model to find an unknown subject’s height. We then worked to try and create equations to calculate an unknown subject's height. The equations worked extremely well for our seven test subjects collected data. However these formulas were designed to be accurate for those seven test subjects and therefore might not be as accurate for unknown subjects.

Our most effective method for estimating an unknown subject's height was Experiment Three → (Using Step Length➗Step # to reach 15ft) to Determine Unknown Height. Experiment three is further explained below. We presented our findings through a slide show in front of the class. We used my mom as an example of how it worked and how our model was extremely accurate in predicting an unknown subject's height using step count and step length.

We found that the SL/S# would increase with the increase in height from our test subjects. Olivia was our shortest test subject, at 5.33ft. Her ratio was the lowest at 0.2879. Jenna was one of our two middle height test subjects, at 5.58ft. Her ratio was 0.3141. Bryce was our tallest test subject, at 6.16ft. His ratio was the largest at 0.3967. Using this information, we were able to begin working on constructing a predictive model.

Content

Gait: The stride of a human as s/he moves his/her limbs.

Step Length: the distance between the point of initial contact of one foot and the point of initial contact of the opposite foot. In normal gait, right and left step lengths are similar. ... Right and left stride lengths are normally equal. Cadence or walking rate is calculated in steps per minute. In our project we used a subject's step length to help determine a ratio which would be used in our predictive model.

Step Count: how many steps it takes to reach a certain distance. In our project we used step count to 15ft to help determine a ratio which would be used in our predictive model.

Height: the measurement from base to top or (of a standing person) from head to foot. In our project we created a model to help predict a subject's height.

Accelerometer: A device that measures the physical acceleration experienced by an object. We used this to collect data for our walking subject's after 60 meters.

G-Force: The gravitational force equivalent, or, more commonly, g-force, is a measurement of the type of force per unit mass – typically acceleration – that causes a perception of weight, with a g-force of 1 g (not gram in mass measurement) equal to the conventional value of gravitational acceleration on Earth, g, of about 9.8. We got this data from the accelerometer, however it did not tell us anything.

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. We created a predictive model for height.

Metric: A quantitative indicator of a characteristic or attribute.

Symmetry: In terms of gait analysis, the quantification of differences between left-foot and right-foot steps.

Variability: lack of consistency or fixed pattern; liability to vary or change. We looked for variability in the data to try and create a predictive model.

Reflection

This project was defiantly a challenge! There are many things I wish to improve on in the future that I learned from this project. The first being better communication. I was extremely ill and could not help my teammates for a week. I came back expecting a lot of progress to be made and nothing was done. This created for a last minute scramble where I as an individual had to figure out the majority of the project. I hope in the future my team will manage their time better and we can equally divide the work load. Also in the future, I want to work on figure out ways to better condense information. That way I can say more while saying less and be able to address more. For this slide show we had a limit of slides so we had to focus on key information. But I still wish I talked more with more detail.

Something I did really well in this project was never give up. When trying to make a predictive model I worked for hours at home, all bye myself trying to figure something out. I ended up creating three predictive methods all by myself. It was hard and took a lot of time, but I never gave up. I challenged myself and used my own knowledge and research to get the job done. I was super proud of myself at the end of the day. I also did a great job at creating the presentation. The presentation was super clean looking and easy to read for the audience. I included minimal words and rarely read off the slide. It was greatly done and personally one of my favorite presentations that I have created.