Song, Xingzhe
(2022)
Enabling Smart Health Applications via Active Acoustic Sensing on Commodity Mobile Devices.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Past decades have witnessed the development and prosperity of mobile and wearable devices with their merits of portability, energy efficiency, and computational capability. Among applications implemented on these devices, smart health is an emerging field that deploys mobile applications to monitor vital signals, manage healthcare records and conduct disease diagnoses. However, existing techniques are limited to measuring health metrics with evident biomarkers such as induced sound or visual change unless dedicated medical sensors are attached. To improve the practicality and feasibility of smart health applications, this thesis aims to utilize active acoustic sensing on subtle biomarkers that are not audible or observable.
This thesis has three major aspects of establishing the active acoustic sensing framework using speaker-microphone pair that are widely available on mobile devices. First, a new system design is proposed to support complete, accurate yet reliable spirometry tests in regular home settings. To achieve this, it measures the chest wall motion based on the sonar system and interprets such motion into lung function indices. Secondly, when the biomarker cannot be intuitively captured as object motion, a channel estimation approach is adopted to quantify muscle tremor induced by muscle fatigue. Lastly, with additional sensing attachments and physiological correlation, active acoustic sensing functionality is further broadened to facial expressions recognition, an intrinsic indicator of mental well-being.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
6 September 2022 |
| Date Type: |
Publication |
| Defense Date: |
12 July 2022 |
| Approval Date: |
6 September 2022 |
| Submission Date: |
1 August 2022 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
96 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Smart Health, Acoustic Sensing, Mobile Computing |
| Date Deposited: |
06 Sep 2022 16:43 |
| Last Modified: |
06 Sep 2022 16:43 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/43303 |
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