DISSERTATION DEFENSE

Author :  Moh. Sabbir Saadat
Advisors: Dr. Sanjib Sur
Date: July 02, 2026
Time: 09:45 Am
Location:  Room 2277, Storey Innovation building

Link:  https://teams.microsoft.com/meet/29819819216139?p=qjSmXRE3Pm9C9PUJ2P

Abstract
Contactless perception systems are an attractive paradigm for applications in security or health monitoring due to their non-intrusiveness and ease of instrumentation compared to wearable or contact-based sensors. This dissertation advances the usability of contactless perception along two connected directions: millimeter-wave (mmWave)-based sensing and imaging, and multimodal digital health assessments.

MmWave signals are high-frequency (24.0 – 300.0 GHz) radio signals that can operate in low visibility, sense through some occlusions, avoid direct camera-like visual capture, and can sense micro-motions that are not possible with conventional vision systems (monocular camera, stereocamera, lidar, IR sensors). Moreover, mmWave signals constitute an important enabling layer in 5Gand-beyond networking paradigms, particularly in indoor networking scenarios. This presents us with an opportunity to bring mmWave sensing for applications where conventional vision systems fail – privacy concerns, low-lighting conditions, requirement of detecting micro-motions, etc. This dissertation addresses a set of fundamental and system-level challenges that hinder the wide adoption of mmWave signals in perception systems: increased and rapid temperature increase due to higher power consumption, information loss resulting from specular reflections, networkingsensing performance trade-offs in integrated system, and motion errors and sparse measurements in hand-held operation.

In the final work of this dissertation, we address the limitation of mmWave system, or any singlemodality perception system, to address perception in more complex digital health applications such as the post-stroke recovery assessment. While mmWave signals capture the fine-grained limb motion abnormality and pose asymmetry in stroke survivors, post-stroke symptoms span across a wider range in terms of scale and functionality: from broad body pose and motor dysfunction to fine-grained limb coordination and facial weakness, to speech and conversational impairments. This dissertation presents a first step towards a multimodal approach to automating post-stroke recovery assessment using vision and audio while retaining mmWave signals as a complementary integration. This work is an interdisciplinary collaboration between our lab and researchers from the University of South Carolina, School of Medicine, paving the way towards research in multimodal digital health sensing on a wider scale.