DISSERTATION DFENSE

Author : Aakriti Adhikari
Advisor: Dr. Sanjib Sur
Date: July 18, 2025
Time: 10:00 am
Place: Room 2265 Innovation building
Teams Link : Join Teams Meeting

Abstract

There is an increasing interest in technologies that can understand and perceive at-home human activities to provide personalized healthcare monitoring, aimed at early detection of disease markers and assisting physicians in making clinical decisions. Existing approaches, such as wearables, require users to wear sensors that can be cumbersome and cause discomfort. Vision based solutions, such as optical cameras, IRs, LiDARs, etc., can be used to design contactless at-home monitoring systems. However, these systems are limited by poor lighting and occlusion, and they are privacy-invasive. Fortunately, high-frequency millimeter-wave wireless devices provide an effective alternative to the existing systems to enable fine-grained health monitoring: Millimeter-wave signals can penetrate certain obstacles, work under zero visibility, and have higher resolution than Wi-Fi. Further, major network providers are actively deploying millimeter-wave technology, a core component of next-generation wireless networks, in both large-scale networks and home routers, thereby paving the way for its widespread adoption in 5G and future devices. This opens up a new opportunity for at-home contactless sensing. But, the eventual success of using millimeter-wave technology for sensing depends on system designs that address the unique challenges of millimeter-wave signals: specularity, variable reflectivity, and low resolution. These issues can lead to incomplete and noisy information about the human subject in the reflected signals, making it difficult to directly estimate human-related information. However, these reflected signals exhibit correlations with various human activities and carry distinct signatures, allowing for the use of data-driven learning models to deduce about humans from the reflected signals.

In this dissertation, we develop data-driven deep learning models to address the fundamental challenges of millimeter-wave sensing. We first design and evaluate deep learning models based on conditional Generative Adversarial Networks to estimate the posture of a person by generating high-resolution human silhouettes and predicting 3D locations of body joints. We then extend the   sensing capabilities to enable contactless sleep monitoring, classifying sleeping states, and predicting sleep postures. Furthermore, we facilitate contactless lung function monitoring by combining wireless signal processing with deep learning, enabling a software-only solution for at-home spirometry tests. Finally, we demonstrate the clinical utility of millimeter-wave sensing through two real-world deployments: a contactless cardiac monitoring system for stroke patients that estimates heart rate and heart rate variability; and  a bed event detection system deployed in hospitals for 24-hour monitoring of high-fall-risk patients, aiming to enable timely interventions and prevent inpatient falls. Together, these systems demonstrate the potential of millimeter-wave sensing to elevate next-generation wireless devices into scalable, privacy-preserving platforms for contactless health monitoring across both home and clinical settings.