Gait Analysis

Overview

A gait is a person's pattern of walking. This project was about gait analysis, which is the systematic study of human motion with the goal of assisting those with conditions that affect their ability to walk, such as helping athletes move more effectively and identifying posture or movement related issues. Using visual observations and movement measurements, we wanted to answer the question: How can we predict someone's weight based on their average acceleration?

In our experiment, we created a table that would allow us to predict someone's weight range based on their average g-force. By using a simple formula of “if x falls between a-b, we predict you will be between c-d pounds,” we can predict this information. We performed analysis on gait data from several human subjects (group members) to understand what properties of a normal walk could be utilized to identify different types of people. By performing walking tests and using accelerometers to collect and graph acceleration vs. time data, we were able to determine what measures related to the human gait could be utilized to predict someone's approximate weight. We ultimately decided that we wanted to use average acceleration as our determining factor because of the patterns that we noticed in the data. This predictive model proved to be successful as we accomplished our goal of anticipating someone's approximate weight.

Evidence of work

Conclusion

Overall the project was a success, but we did encounter struggles throughout our gait analysis process. Initially, our initial formula took into account the variable we were predicting. As it incorporated weight, it was obvious that this would not be a possible model for predicting other group members' weights. It was a continued struggle to find patterns among the data to create our predictive model. We spent many hours experimenting and trying out different calculations. We took a step back and decided to use a simple table instead of a lengthy equation for our model. While this strategy might have reduced the accuracy of our model slightly, we are confident that finding another way would have taken an unnecessary and obscene amount of time. This project was a great lesson in perseverance and critical thinking. It took the entire team to compartmentalize the report, analysis, and presentation. We are very proud of our achievements and what we have learned. We now are more confident in our ability to synthesize lots of data and collaborate as a team in a goal to make the best Capstone project possible.