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Predictive modeling of health status using motion analysis from mobile phones
Cheng, Qian
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https://hdl.handle.net/2142/97278
Description
- Title
- Predictive modeling of health status using motion analysis from mobile phones
- Author(s)
- Cheng, Qian
- Issue Date
- 2017-03-29
- Director of Research (if dissertation) or Advisor (if thesis)
- Schatz, Bruce R.
- Doctoral Committee Chair(s)
- Schatz, Bruce R.
- Committee Member(s)
- Han, Jiawei
- Smaragdis, Paris
- Konig, Christian
- Department of Study
- Computer Science
- Discipline
- Computer Science
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Date of Ingest
- 2017-08-10T19:14:34Z
- Keyword(s)
- Mobile health
- Smartphone application
- Medical information retrieval
- Chronic disease
- Abstract
- It is unknown what physiological functions can be monitored at clinical quality with a normal smartphone, which is ubiquitous. There are standard measures like walk speed, pulmonary function and oxygen saturation variation for health status of cardiopulmonary patients. The dissertation is to summarize my studies of using sensor data from regular smartphones to accurately measure walking patterns, in order to monitoring health status for cardiopulmonary patients. Fifty five pulmonary patients were participated in the study. The sensor data for their walk test and free walk are collected and stored by a novel designed Android smartphone application. Different machine learning techniques are applied and compared to predict gait speed, pulmonary function and oxygen saturation. The result shows that walking patterns are highly correlated with health status. The trained models can predict health status accurately for each patient. Initial testing indicates the same high accuracy as with active monitors, for patients in hospitals during walk tests. The ultimate goal is that patients can simply carry their phones during everyday living, while models support automatic prediction of pulmonary function for health monitoring.
- Graduation Semester
- 2017-05
- Type of Resource
- text
- Permalink
- http://hdl.handle.net/2142/97278
- Copyright and License Information
- Copyright 2017 Qian Cheng
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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisDissertations and Theses - Computer Science
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