Leveraging AI and radar for non-invasive fine-grained human activity monitoring

Alhumaily, Basim (2026) Leveraging AI and radar for non-invasive fine-grained human activity monitoring. PhD thesis, University of Glasgow.

Abstract

Human-centric monitoring demands sensing technologies that are non-invasive, privacy-preserving, and capable of operating reliably across diverse environments and motion scales. Conventional approaches based on wearable sensors suffer from compliance and long-term usability constraints, while vision-based systems raise privacy concerns and degrade under poor lighting or occlusion. Alternative RF-based modalities, such as Wi-Fi channel state information (CSI), offer privacy-preserving device-free sensing but remain sensitive to environmental dynamics and offer limited spatial resolution. In contrast, frequency-modulated continuous-wave (FMCW) radar provides a favourable balance of robustness, privacy preservation, and fine-motion sensitivity, making it well-suited to the human-monitoring use cases targeted in this thesis, namely, ambient assisted living and in-vehicle driver monitoring, across behavioural and physiological motion scales. This thesis positions radar-based sensing as a unified framework for large-scale and fine-grained human activity monitoring and vital signs estimation, and further examines how behavioural and physiological cues can be complemented through multimodal fusion in realistic settings. First, large-scale human activity monitoring is addressed using FMCW radar representation maps, where multiple radar signal-processing domains and deep learning architectures are systematically evaluated under subject-wise testing. Among the evaluated configurations, MobileNetV2 combined with short-time Fourier transform (STFT) representations achieves a recognition accuracy of 96.30% with the shortest inference time of 2.57 ms per sample, while requiring only 0.22 s to preprocess and generate each STFT-based time–frequency radar map, supporting real-time, low-power deployment. Second, the thesis advances fine-grained monitoring by analysing seated upper-body and head movements using mmWave FMCW radar. Generalisation to unseen subjects is initially limited to 60% accuracy; by applying the proposed data augmentation strategy, performance improves substantially to 91.32%, demonstrating robust cross-subject recognition under data-scarce conditions. In parallel, a motion-robust radar-based vital signs estimation framework is developed, which uses variational mode decomposition to suppress motion artefacts. Under non-stationary conditions, vital signs estimation performance improves by 40.59%, highlighting radar’s ability to recover physiological dynamics despite concurrent movement. Finally, the thesis extends toward multimodal human state monitoring by integrating physiological heart rate with behavioural gaze metrics in real-world driving. Analysis of approximately 480 minutes of naturalistic driving data reveals clear scenario-dependent and subject-specific patterns, and a lightweight decision-level fusion algorithm identifies abnormal driving segments with improved robustness and interpretability compared with unimodal cues, demonstrating effective operation under real-world, unconstrained conditions.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Human activity monitoring, large-scale, fine-grained activity monitoring, physiological-scale monitoring, radar sensing, data scarcity, data augmentation, transfer learning, multimodal human monitoring.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science; T Technology > TK Electrical engineering. Electronics Nuclear engineering
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Zoha, Dr. Ahmed, Imran, Professor Muhammad, Hussain, Professor Sajjad and Mohjazi, Dr. Lina
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-86167
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 13 Aug 2026 10:40
• Last Modified: 13 Aug 2026 12:32
• Thesis DOI: 10.5525/gla.thesis.86167
• URI: https://theses.gla.ac.uk/id/eprint/86167

Related URLs

• Article DOI
• Article DOI
• Conference proceeding
• Conference proceeding
• Conference proceeding
• Pre-print