Shahzad Ahmad

• Ahmad, Shahzad; Mukherjee, Surajit; Chanda, Sukalpa; Palaiahnakote, Shivakumara; Pal, Umapada & Pedersen, Marius (2025). PHASE: Physiological Dynamics-Based Attention for SpO$$_2$$ Estimation. I Petersen, Jens & Dahl, Vedrana Andersen (Red.), Image Analysis: 23rd Scandinavian Conference, SCIA 2025, Reykjavik, Iceland, June 23–25, 2025, Proceedings, Part I. Springer Nature Switzerland. ISSN 9783031959103. s. 30–44. doi: 10.1007/978-3-031-95911-0_3. Fulltekst i vitenarkiv Vis sammendrag

  The two-volume set 15725 and 15726 constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2025, held in Reykjavik, Iceland, during June 23–25, 2025. The 60 revised full papers presented in these proceedings were carefully reviewed and selected from 98 submissions. Part I: Machine learning and deep learning; Segmentation, Grouping, and Shape; Vision for robotics and autonomous vehicles; Biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; and Vision applications and systems.

• Ahmad, Shahzad; Chanda, Sukalpa & Rawat, Yogesh S. (2025). T2L: Efficient Zero-Shot Action Recognition with Temporal Token Learning. Transactions on Machine Learning Research (TMLR). Fulltekst i vitenarkiv

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• Ahmad, Shahzad; Chanda, Sukalpa; Pedersen, Marius & Rawat, Yogesh S (2026). Remote Intelligent Health Monitoring. Norges teknisk-naturvitenskapelige universitet. ISSN 9788235300881. Fulltekst i vitenarkiv Vis sammendrag

  Remote health monitoring using non-contact visual sensing has emerged as a promising direction for scalable, accessible, and privacy-preserving healthcare technologies. However, reliable estimation of physiological signals from facial videos remains challenging due to motion artifacts, illumination variation, environmental noise, and limited supervision. In parallel, meaningful interpretation of physiological change in realistic settings also requires awareness of behavioral and contextual information. This thesis develops a coherent research framework for Remote Intelligent Health Monitoring (RIHM), organized around two complementary branches: non-contact physiological sensing and contextual video understanding. The first part of the thesis focuses on remote photoplethysmography (rPPG) and camera-based SpO2 estimation. It shows that temporal stability is a foundational requirement for reliable physiological representation learning from weak and indirectly observed video signals. To address this challenge, the thesis introduces stability-aware learning strategies and continuous-time neural modeling through Liquid Time-Constant (LTC) networks, enabling adaptive temporal representations that improve robustness to motion, noise, and signal inconsistency. For camera-based SpO2 estimation, the thesis further develops physiology-aware modeling of AC/DC signal components together with continuity-regularized and physics-aware learning objectives, improving temporal coherence and physiological plausibility. The second part of the thesis focuses on contextual video understanding through zero-shot and compositional action recognition using vision-language models. To improve temporal reasoning in video, lightweight temporal adaptation mechanisms are introduced that enhance motion sensitivity without requiring full retraining of large pretrained backbones. To improve compositional generalization and reduce shortcut bias, the thesis further develops explanation-conditioned and composition-aware learning strategies that strengthen robustness to unseen action-object combinations. Taken together, the thesis shows that reliable remote health intelligence from video benefits from structured modeling of latent dynamics. In the physiological branch, this structure appears through temporal stability, continuous-time dynamics, physiological priors, and continuity-aware constraints. In the contextual branch, it appears through temporally efficient adaptation and shortcut-aware compositional reasoning. Rather than presenting a fully integrated end-to-end monitoring system, the thesis establishes the methodological foundations and conceptual bridge needed for future RIHM systems that combine physiological sensing with contextual understanding in a more reliable, interpretable, and scalable manner.

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