A Mobile Application for Monitoring Community Mobility in Children with Osteogenesis Imperfecta
Mentor 1
Jacob Rammer
Start Date
1-5-2020 12:00 AM
Description
Children with Osteogenesis Imperfecta (OI) have increased bone fragility and low bone mass. They face a significant risk of bone fractures during everyday activities, which can cause reduced participation in the community and with their peers. Though much research has been conducted with patients in various stages of OI, there is less information about real-world activities and mobility in children with OI. This study will identify the best technological method to track mobility in this community by developing and testing a prototype mobile application. Prior to the development of the app, studying the capability of Android/iPhone built-in sensors (accelerometer, gyroscope, magnetometer, GPS, etc.) is essential as these sensors are the main source to capture human motion. The prototype mobile application uses built-in sensors in a smartphone to collect and store the motion of children with OI. The sensor data that is stored in the mobile app can be related to gait analysis via machine learning algorithms. Through this study, the data results can provide crucial information about current children with OI and can be used as a starting point for future research activities in this OI population.
A Mobile Application for Monitoring Community Mobility in Children with Osteogenesis Imperfecta
Children with Osteogenesis Imperfecta (OI) have increased bone fragility and low bone mass. They face a significant risk of bone fractures during everyday activities, which can cause reduced participation in the community and with their peers. Though much research has been conducted with patients in various stages of OI, there is less information about real-world activities and mobility in children with OI. This study will identify the best technological method to track mobility in this community by developing and testing a prototype mobile application. Prior to the development of the app, studying the capability of Android/iPhone built-in sensors (accelerometer, gyroscope, magnetometer, GPS, etc.) is essential as these sensors are the main source to capture human motion. The prototype mobile application uses built-in sensors in a smartphone to collect and store the motion of children with OI. The sensor data that is stored in the mobile app can be related to gait analysis via machine learning algorithms. Through this study, the data results can provide crucial information about current children with OI and can be used as a starting point for future research activities in this OI population.
