From Systemic to Regional: Personal Health and Medical Monitoring Systems that Adapt to Individual Variance
Name
zhu-junyizhu-phd-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
71.44 MB
Format
Adobe PDF
Checksum (MD5)
f9e38c7cad899509992bb4c2f16984a8
Author(s)
Zhu, Junyi
Advisor(s)
Mueller, Stefanie
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Doctors require biometric sensor data to improve diagnostic accuracy, monitor a patient’s recovery progress, and make informed decisions about further treatment. Advances in electronics and sensing technologies have led to the development of remote monitoring devices, such as for ECG and blood pressure, which can collect biometric data outside of the clinic. However, these forms of systemic biometric signal monitoring only capture limited aspects of one's overall health, lacking detailed information on specific local body regions. In addition, individual patient health conditions are diverse and often complex. Thus, traditional sensing techniques, while effective for the broader population, often do not meet the unique needs of specific patient groups, especially for environments beyond clinic and home.
In this thesis, I will be presenting my Ph.D. work around personalized health and medical monitoring systems that adapt to individual variance, including muscle engagement monitoring during unsupervised rehabilitation, upper airway obstruction monitoring for obstructive sleep apnea, as well as device and measurement setup customization based on the patient’s regional body physique and use environment.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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