Advancing ambient intelligence in healthcare : granularity, efficiency, and privacy
Abstract/Contents
- Abstract
- Artificial Intelligence (AI) is revolutionizing the healthcare industry by providing cutting-edge solutions that can significantly enhance patient care and medical research. However, this wave of technological transformation has largely overlooked the physical environment of clinical care delivery. This dissertation illuminates the significant impact of integrating video activity recognition, powered by advanced machine learning and intelligent sensors, into ICU and residential care, demonstrating its potential to alleviate healthcare burdens and improve patient outcomes. We first detail how healthcare spaces can be equipped with ambient intelligence using smart sensors and machine learning algorithms. We then introduce three crucial technical foundations that enable this application. Firstly, we instantiate a novel task, benchmark, and model for discerning complex human activities, offering a hierarchical comprehension of actor roles, object attributes, and their relationships. Secondly, we unveil a suite of data and label-efficient algorithms tailored to address overcome the scarcity of annotated data, a challenge amplified by privacy constraints, proprietary considerations, and the substantial costs associated with domain-specific annotation. Finally, we embark on a comprehensive dialogue on ensuring trustworthy machine learning from ethical and privacy perspectives. We introduce a scalable, differentially private algorithm, specifically designed for large-scale video activity recognition tasks. To demonstrate the real-world impact of our ambient intelligence system, we conclude the dissertation by showcasing two clinical deployments - one within the hospital environment and one in daily living spaces.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Luo, Zelun |
|---|---|
| Degree supervisor | Li, Fei-Fei |
| Thesis advisor | Li, Fei-Fei |
| Thesis advisor | Adeli,Ehsan |
| Thesis advisor | Wu, Jiajun |
| Thesis advisor | Yeung, Serena |
| Degree committee member | Adeli,Ehsan |
| Degree committee member | Wu, Jiajun |
| Degree committee member | Yeung, Serena |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zelun Luo. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/dx345zf0386 |
Access conditions
- Copyright
- © 2023 by Zelun Luo
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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