Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Privacy Concerns in Machine Learning Fall Prediction Models: Implications for Geriatric Care and the Internet of Medical Things

Version 1 : Received: 12 March 2022 / Approved: 15 March 2022 / Online: 15 March 2022 (10:40:36 CET)

How to cite: Yang, R. Privacy Concerns in Machine Learning Fall Prediction Models: Implications for Geriatric Care and the Internet of Medical Things. Preprints 2022, 2022030202 (doi: 10.20944/preprints202203.0202.v1). Yang, R. Privacy Concerns in Machine Learning Fall Prediction Models: Implications for Geriatric Care and the Internet of Medical Things. Preprints 2022, 2022030202 (doi: 10.20944/preprints202203.0202.v1).

Abstract

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy.

Keywords

machine learning; artificial intelligence; computer vision; cybersecurity; privacy, security; gerontology; social gerontology; internet of medical things; best practices

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.