Date of Award

May 2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Biomedical Engineering

First Advisor

Ryan Striker

Second Advisor

Enrique Alvarez Vazquez

Abstract

The rapid aging of the global population necessitates scalable, non-invasive solutions, for monitoring Activities of Daily Living (ADLs) to detect early signs of health decline. This thesis presents a novel, end-to-end framework for monitoring bathroom activities—specifically bathing, toileting, and oral hygiene—using wearable sensors, machine learning and Agentic Artificial Intelligence (AI). The research addresses three critical gaps: the lack of granular activity recognition in privacy-sensitive spaces, the need for personalized health insights without manual logging, and the cybersecurity risks inherent in deploying AI in healthcare. First, algorithms utilizing wearable humidity and accelerometer sensor data were developed to classify hygiene behaviors with high precision. Second, a cross-platform mobile application was engineered to log these events and support a Retrieval-Augmented Generation (RAG) recommender system as proof of concept. Finally, a “dual-agent guardrail” security framework was implemented to protect against prompt injection and jailbreaks. This work demonstrates that secure, autonomous health monitoring is achievable using commodity hardware like the CarePredict Tempo device, offering viable pathway for aging-in-place technologies.

Download

Share

COinS