Project Objective
Design an experiment to collect data on possible measures, correlations, and, predictions of a person's gait.
Key Terms and Concepts
Accelerometer: A device that measures the physical acceleration experienced by an object.
Dynamicity: Quantification of kinetic parameters.
Gait: The way a something moves.
Metric: Quantitative indicator of a characteristic or attribute.
Mathematical Model: An equation that describes an observation or predicts the behavior a system.
Symmetry: Quatification of Left and Right foot steps.
Variability: Quantification of stride fluctuations.
R^2 Value (Coefficient of Determination): The extent to which the dependent variable's variability is accounted by the independent variable; a measure of how well a regression model describes the data points.
Dynamicity: Quantification of kinetic parameters.
Gait: The way a something moves.
Metric: Quantitative indicator of a characteristic or attribute.
Mathematical Model: An equation that describes an observation or predicts the behavior a system.
Symmetry: Quatification of Left and Right foot steps.
Variability: Quantification of stride fluctuations.
R^2 Value (Coefficient of Determination): The extent to which the dependent variable's variability is accounted by the independent variable; a measure of how well a regression model describes the data points.
Project Materials
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Mini-Presentation
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Lab Report
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Data
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Gantt Chart
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Reflection
This project was a little challenging in terms of scheduling and team member availability. While we were working on this project, our team was also getting possible mentor contacts, logo design, and brainstorming our capstone project; and some of our team members were involved with metalworking for Project Pegasus, with the OP-51 Training. With attention divided and members missing, our team was able to make progress on this project with good focus and personal accountability, especially at the introductory stages of this project. Although we were very productive with understanding the basics of this project, we could have planned some agenda over the time we were working to continue our focus. We did divide our tasks into data collection and data analysis, but had we been as procedural with what we wanted to accomplish each day with the data analysis as the data collection, we could have gotten better results. More trials, consistent trials, and class-wide collaboration with the same procedure could have been run to make our predictive model, correlation strengths, and data analysis better. We could have also modified our procedure to have the phones more tightly wrapped around our subjects to reduce acceleration noise in the data from the movement of the phone accelerometer. We were also able to divide tasks among ourselves between these projects, which allowed for a smoother workflow.
- Nihal Nazeem.
- Nihal Nazeem.