Doctoral Dissertations

Abstract

"The human body represents a rich source of physiological and behavioral information, where precise analysis of anatomy, body parts, 3D pose, and motion enables cross-disciplinary precision applications. This dissertation formulates this challenge as a human modeling problem, where proposed algorithms convert the human body into a three-dimensional digital twin capturing anatomical structure and pose. These representations are further analyzed using additional algorithms to enable precision applications. To realize this vision, LiDAR sensing is adopted due to its privacy-preserving nature, robustness to lighting conditions, color-blind sensing characteristics, and decreasing cost.

Human modeling using LiDAR is challenging due to sparse, irregular, and unordered point-cloud data that lack explicit anatomical structure and often suffer from partial observations and pose variability. To address these challenges, this dissertation introduces two novel methodologies. First, a refinement-based 3D human pose estimation framework formulates LiDAR pose estimation as a prior-to-posterior refinement problem that significantly enhances pose estimation performance. Second, an anatomy-guided serialization approach transforms unordered point-cloud data into structured, human-aware representations by incorporating anatomical relationships, enabling accurate human body-part segmentation.

To enable reliable operation in dynamic multi-person environments, this dissertation formulates human identification as a wireless localization problem and proposes novel localization algorithms for challenging conditions. This framework leverages 6G technologies, including millimeter-wave and reconfigurable intelligent surfaces, along with LiDAR-assisted sensing. Additionally, dedicated datasets for 3D pose estimation and infant body-part segmentation, along with synthetic datasets, are introduced for evaluation.

Using the proposed modeling algorithms, sensing infrastructure, and datasets, this framework enables human-centered applications. In particular, the proposed methods are applied to contactless respiratory monitoring and human gesture recognition, demonstrating the effectiveness of LiDAR-based human modeling"-- Abstract, p. iii

Advisor(s)

Alsharoa, Ahmad
Zawodniok, Maciej Jan, 1975-

Committee Member(s)

Dagli, Cihan H., 1949-
Baker, Denise A.
Esmaeelpour, Mina
Sedigh, Sahra

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Publisher

Missouri University of Science and Technology

Publication Date

2026

Pagination

xiv, 183 pages

Note about bibliography

Includes_bibliographical_references_(pages 161-182)

Rights

© 2026 Omar Rinchi , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12625

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