College - Author 1
College of Engineering
Department - Author 1
Biomedical Engineering Department
Degree Name - Author 1
BS in Biomedical Engineering
College - Author 2
College of Engineering
Department - Author 2
Biomedical Engineering Department
Degree - Author 2
BS in Biomedical Engineering
College - Author 3
College of Engineering
Department - Author 3
Biomedical Engineering Department
Degree - Author 3
BS in Biomedical Engineering
Date
3-2023
Primary Advisor
Britta Berg-Johansen, College of Engineering, Biomedical Engineering Department
Abstract/Summary
This document serves to outline the design process and final design for our project to address elderly independent living in collaboration with Apple. Included in this document is necessary academic literature of current fall risk assessments and descriptions of the current market of wearable gait analysis devices. Based on this research and data, we then outline a plan for design, manufacturing, prototyping, and testing a device to fulfill the original prompt given by our sponsors: create a device that will increase the ability of an aging loved one to live at home safely. This device is a wearable that collects gait data of elderly people and uses that data to predict their risk of falling. Key customer requirements include increasing elder independence, predicting fall risk, and detecting changes in gait and balance. This led to a number of engineering specifications, the most important of which being time of supervision of the elderly person, displacement of device with movement, and the reliability of the sensors to accurately measure metrics associated with gait. In order to achieve these targets, we developed a workflow to span the next two quarters.
We created morphology sketches based on four different functions we decided were necessary for our device to function and created three different concepts based off those functions. All three concepts were analyzed against engineering metrics, and a single concept was chosen: a shoe with built in ultrasonic sensors and an IMU for foot height and gait analysis. Concept sketches were designed to model how this concept would function using SolidWorks, and failure mode and effects analysis was performed to find potential failures before we finalized the device design in order to prevent them from being designed into the product. We also conducted failure mode and effects analysis in order to detect possible problems that could be designed into the device unintentionally and took steps to eliminate the possible risks.
We then created a detailed design that lists exactly what our prototype contains along with how it functions and looks. A prototype was built based on the revised manufacturing plan, circuit diagrams, and design parameters. This prototype was evaluated based on the detailed test plans that include sample sizes, test protocols and expected results. The purpose of these tests was to verify that our device functions in the way that is expected and to validate that the data we are collecting is accurate. Correlation coefficients were determined to be higher than 0.4 for acceleration data of the device IMU when compared to the iPhone IMU, meaning they are strongly correlated with each other and suggesting any algorithms used with the iPhone may also be used for our device. Correlation coefficients were even higher for angular velocity collection, being 0.7 and higher, leading to a very strong correlation between the two IMUs and furthers the algorithm suggestion. Additional tests were to ensure the safety of the user at all times. Data collection was analyzed and then statistical analysis was performed to determine whether the prototype fulfilled the goals presented by our sponsor. Analysis tests determined that IMU acceleration and angular velocity were capable of detecting changes in gait, but ultrasonic sensors were not able to do so.
We conclude with a discussion of learnings from the prototyping process and suggested future steps with a focus on development of a more substantial data analysis algorithm.
URL: https://digitalcommons.calpoly.edu/bmedsp/174