This application was designed to measure walking speed accurately and precisely on an iOS device to improve accessibility to this clinically relevant information.
Sponsor
Dr. Everett Hills, Dr. Elizabeth Starkey
Team Members
Yasmin Ali | Thomas England | Nicholas Fink | Benjamin Hommrich | Rishabh Tiwari | Shane Ward | Thomas Williams | | | | |
Project Poster
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Project Summary
Overview
Walking speed is important for measuring functional decline, leading to fall risk, and requiring physical rehabilitation. Current methods are outdated or imprecise or require expensive equipment. We developed an app that uses the LiDAR feature of the newest generation of Apple products to markedly improve the precision and accuracy of measuring walking speed.
Objectives
– Utilize the LiDAR sensor in new Apple products to measure walking speed precisely and accurately
– Capture a simultaneous video of the patient walking for a qualitative assessment
– Create a proof-of-concept iOS app designed for easy use by any health-minded user
Approach
– Machine-learning technology locates user joints to track motion
– Shoulder and hip points are used to locate a center of mass point
– LiDAR sensors in Apple technologies tracks distance to the center of mass
– Using distance measurements and time between 2 meters and 6 meters, an average walking speed is calculated
– Simultaneous video recording allows for qualitative gait analysis
– After testing is completed, users can enter areas of the body where they are experiencing pain and comment on how their bodies are feeling
– User interface is designed for easy use by any user
– Users can view past records and observe trends over time
Outcomes
– Accuracy and precision of the LiDAR in the Apple iPhone 12 is comparable to research-compatible LiDAR
– This method offers improved accuracy over conventional methods using a stopwatch
– Using the Apple iPhone 12 provides a potential means to collect a functionally important vital sign that is not often obtained in visits to the doctor
– This app improves the capabilities for remote patient monitoring