Advisor Information
Hesham Ali
Location
UNO Criss Library, Room 249
Presentation Type
Oral Presentation
Start Date
3-3-2017 1:30 PM
End Date
3-3-2017 1:45 PM
Abstract
The general relationship between mobility/gait patterns and health conditions, has been established by many research studies. Gait patterns have been used in predicting fall incidents, assessing the movement of people with Parkinson disease and assessing gait characteristics of patients with major depression. However, not much has been reported in terms of how to utilize mobility data along with gait patterns to assess health levels and to identify early stages of certain diseases or conditions that impact mobility/gait patterns. In this project, we propose to develop a population analysis system based on recent Big Data technologies that relies on different types of mobility collected seamlessly using wearable wireless devices. The proposed system utilizes a dynamic learning system to analyze mobility data and predict potential health hazards.
On Utilizing Big Data to Assess Health Levels
UNO Criss Library, Room 249
The general relationship between mobility/gait patterns and health conditions, has been established by many research studies. Gait patterns have been used in predicting fall incidents, assessing the movement of people with Parkinson disease and assessing gait characteristics of patients with major depression. However, not much has been reported in terms of how to utilize mobility data along with gait patterns to assess health levels and to identify early stages of certain diseases or conditions that impact mobility/gait patterns. In this project, we propose to develop a population analysis system based on recent Big Data technologies that relies on different types of mobility collected seamlessly using wearable wireless devices. The proposed system utilizes a dynamic learning system to analyze mobility data and predict potential health hazards.