System for K-Level Assessment in Amputees:
Turning Qualitative into Quantified Results.
Connor Bortz, Matthew Galbraith, Jessica Lewis, Chelsea Madden, Anthony Rossi
Overview
Concept Generation
Final Design
Background Information
Product Overview
Prototype
Current Problem:
• Amputees nationwide have trouble gaining Medicare
approval
• Data that is analyzed is too qualitative
• Need to develop a method that uses tangible data to
justify patient’s K-Level selection
End Goal:
• Implement a device that collects ambulation data
throughout the day and effectively display this data
• K-level designation will be based on data collected
Fitbit One:
• Uses three-dimensional accelerometer to
detect motion
• Measures relevant parameters such as steps
taken, distance traveled, and elevation
• Able to record and store ambulation data for
periods up to 10 days
• Clips onto most articles of clothing
• Commercially available for less than $100
The Product
The FitBit® “One”, a commercially available product
has the capabilities needed for the project.
Project Scope
To develop a system to measure various gait
parameters that pertain to ambulating to be used for
diagnosis justification, and prosthetic prescription.
Metrics
Metric
Time
Budget
Description
The device must be completed before the
final deadline.
The total development and prototype
cost cannot exceed the budget.
Compatibility
The device will be compatible with the
parts of existing prosthetics.
Number of Steps
The device will measure the number of
steps the user takes over a certain period
of time.
Speed
The device will measure the speed of the
user in meters per second.
Distance
The design will measure the distance the
user covers in meters.
Incline/Decline
The device will recognize whether the
user travels over an incline or decline.
Data Acquisition
The device will be able to store data over
a certain amount of time. This data will
be able to be uploaded in a certain
amount of time.
Cost
The device should be sold to consumer
below target value.
Portability
The device will stay within a reasonable
weight and size.
User Readability
The device will have a reasonable screen
size.
Testing Process
Fitbit accuracy
had to be
Able-bodied test
subject wore Fitbit
verified
Subject was asked
to perform various
activities
Comparison
Fitbit values were
Assistant recorded
validated Fitbit’s
compared to
actual ambulation
accuracy
actual values
parameters values
Problem with Data Collection
Traditional Fitbit data
collection process:
1. Data is collected by the Fitbit
2. The user uploads the
information the device has
collected to the Fitbit database
3. These values are displayed
directly from the Fitbit website
Problems with this method
include:
• The smallest increment of time
the data can be viewed is one
day
• The cadence change of the user
cannot be calculated
• There is little customization
with how the data is displayed
• Cannot handle large volume of
patients.
Acknowledgements
We would like to thank our sponsor, John Horne, our advisor
Dr. Buckley, and the rest of the Senior Design Staff.
Prototype data collection process:
1. Data from the Fitbit is extracted and stored in the cloud
2. Fitabase converts the data into an intraday csv file
3. Ambulation data is processed and produces K-Level
Diagnosis.
In addition to the prototype, the experiments and clinical
trials were carefully designed as well. In order for the
program to be implemented, necessary clinical trials will be
used for validation of the device’s use in the prosthetic
industry.
Testing Plan
The accuracy of the Fitbit and capabilities of Fitabase were
tested through various simulations. The purpose of the
simulations was to emulate different K-levels, specifically K-2
and K-3. These two levels are the most difficult to
differentiate, causing the most problems during diagnosis and
Medicare applications.
Validation
The testing validated that the Fitbit was indeed accurate and
Fitabase was able to capture the appropriate data to
differentiate simulated K-2 and K-3 patients.
Path Forward
Next, the Fitbit will be used on actual amputee patients in
order to gather data and normalize across the different Klevels. The finalized software will then predict a patients Klevel based on ambulation data acquired by the Fitbit, which
will help diagnosis and Medicare approval.