RF SENSORS FOR MEDICAL AND CYBER-PHYSICAL INTELLIGENCE
| dc.contributor.author | Zhang, Zijing | |
| dc.contributor.chair | Kan, Edwin | en_US |
| dc.contributor.committeeMember | Krishnamurthy, Vikram | en_US |
| dc.contributor.committeeMember | Doerschuk, Peter | en_US |
| dc.date.accessioned | 2024-01-31T21:20:18Z | |
| dc.date.available | 2024-01-31T21:20:18Z | |
| dc.date.issued | 2023-05 | |
| dc.description.abstract | My research has focused on continuous and non-invasive sensing of physiological signals including respiration, muscle activities, heartbeat dynamics, and other biological signals. I seek to establish a touchless RF sensor that can be implemented as wearables on users, or integrated into the furniture to become invisible to the user. Such sensor can greatly enhance data continuity, comfort and convenience to enable many healthcare applications, especially for at-home continuous diagnosis and prognosis, with less reliance on subjective self report. My research utilized machine-learning (ML) algorithms that can take the physiological data from our sensors to provide holistic diagnostics and prognosis. This sensor has been applied to pulmonary diseases including COVID-19 and chronic obstructive pulmonary diseases (COPD) to help identify dyspneic exacerbation, leading to early intervention and possibly improving outcome. The sensor has also been applied to prevalent sleep disorders such as apnea and hypopnea. Another aspect of my research focuses on muscle monitoring. Conventional electromyography (EMG) measures the neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. I proposed radiomyography (RMG), a novel muscle wearable sensor that can non-invasively and continuously capture muscle contraction in various superficial and deep layers. Continuous monitoring of individual skeletal muscle activities has significant medical and consumer applications, including detection of muscle fatigue and injury, diagnosis of neuromuscular disorders such as the Parkinson’s disease, assessment for physical training and rehabilitation, and human-computer interface (HCI) applications. I verified RMG experimentally on a forearm wearable sensor for extensive hand gesture recognition, which can be applied to various applications including assistive robotic control and user instructions. I also demonstrated a new radiooculogram (ROG) for non-invasive eye movement monitoring with eyes open or closed. ROG is promising for gaze tracking and study of sleep rapid eye movement (REM). | en_US |
| dc.identifier.doi | https://doi.org/10.7298/g8k8-vx11 | |
| dc.identifier.other | Zhang_cornellgrad_0058F_13581 | |
| dc.identifier.other | http://dissertations.umi.com/cornellgrad:13581 | |
| dc.identifier.uri | https://hdl.handle.net/1813/114183 | |
| dc.language.iso | en | |
| dc.subject | digital healthcare | en_US |
| dc.subject | human computer interaction | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | RF sensor | en_US |
| dc.subject | vital signs monitoring | en_US |
| dc.title | RF SENSORS FOR MEDICAL AND CYBER-PHYSICAL INTELLIGENCE | en_US |
| dc.type | dissertation or thesis | en_US |
| dcterms.license | https://hdl.handle.net/1813/59810.2 | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Cornell University | |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph. D., Electrical and Computer Engineering |
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