Karam Tomotaki-Dawoud

Katerina Katsarou, George Zountsas, Karam Tomotaki-Dawoud et al.

Reliable monitoring of surgical instrument exchanges is essential for maintaining procedural efficiency and patient safety in the operating room. Automatic detection of instrument handovers in intraoperative video remains challenging due to frequent occlusions, background clutter, and the temporally evolving nature of interaction events. We propose a spatiotemporal vision framework for event-level detection and direction classification of surgical instrument handovers in surgical videos. The model combines a Vision Transformer (ViT) backbone for spatial feature extraction with a unidirectional Long Short-Term Memory (LSTM) network for temporal aggregation. A unified multi-task formulation jointly predicts handover occurrence and interaction direction, enabling consistent modeling of transfer dynamics while avoiding error propagation typical of cascaded pipelines. Predicted confidence scores form a temporal signal over the video, from which discrete handover events are identified via peak detection. Experiments on a dataset of kidney transplant procedures demonstrate strong performance, achieving an F1-score of 0.84 for handover detection and a mean F1-score of 0.72 for direction classification, outperforming both a single-task variant and a VideoMamba-based baseline for direction prediction while maintaining comparable detection performance. To improve interpretability, we employ Layer-CAM attribution to visualize spatial regions driving model decisions, highlighting hand-instrument interaction cues.

Birgit Nierula, Karam Tomotaki-Dawoud, Daniel Johannes Meyer et al.

We present the early-stage design and implementation of a multimodal, real-time communication analysis system intended as a foundational interaction layer for adaptive VR training. The system integrates five parallel processing streams: (1) verbal and prosodic speech analysis, (2) skeletal gesture recognition from multi-view RGB cameras, (3) multimodal affective analysis combining lower-face video with upper-face facial EMG, (4) EEG-based mental state decoding, and (5) physiological arousal estimation from skin conductance, heart activity, and proxemic behavior. All signals are synchronized via Lab Streaming Layer to enable temporally aligned, continuous assessments of users' conscious and unconscious communication cues. Building on concepts from social semiotics and symbolic interactionism, we introduce an interpretation layer that links low-level signal representations to interactional constructs such as escalation and de-escalation. This layer is informed by domain knowledge from police instructors and lay participants, grounding system responses in realistic conflict scenarios. We demonstrate the feasibility and limitations of automated cue extraction in an XR-based de-escalation training project for law enforcement, reporting preliminary results for gesture recognition, emotion recognition under HMD occlusion, verbal assessment, mental state decoding, and physiological arousal. Our findings highlight the value of multi-view sensing and multimodal fusion for overcoming occlusion and viewpoint challenges, while underscoring that fusion and feedback must be treated as design problems rather than purely technical ones. The work contributes design resources and empirical insights for shaping human-AI-powered XR training in complex interpersonal settings.

Anand Umashankar, Karam Tomotaki-Dawoud, Nicolai Schneider

Remote sensing archives are inherently distributed: Earth observation missions such as Sentinel-1, Sentinel-2, and Sentinel-3 have collectively accumulated more than 5 petabytes of imagery, stored and processed across many geographically dispersed platforms. Training machine learning models on such data in a centralized fashion is impractical due to data volume, sovereignty constraints, and geographic distribution. Federated learning (FL) addresses this by keeping data local and exchanging only model updates. A central challenge for remote sensing is the non-IID nature of Earth observation data: label distributions vary strongly by geographic region, degrading the convergence of standard FL algorithms. In this paper, we conduct a systematic empirical study of three FL strategies -- FedAvg, FedProx, and bulk synchronous parallel (BSP) -- applied to multi-label remote sensing image classification under controlled non-IID label-skew conditions. We evaluate three convolutional neural network (CNN) architectures of increasing depth (LeNet, AlexNet, and ResNet-34) and analyze the joint effect of algorithm choice, model capacity, client fraction, client count, batch size, and communication cost. Experiments on the UC Merced multi-label dataset show that FedProx outperforms FedAvg for deeper architectures under data heterogeneity, that BSP approaches centralized accuracy at the cost of high sequential communication, and that LeNet provides the best accuracy-communication trade-off for the dataset scale considered.

Karam Tomotaki-Dawoud, Anna Hilsmann, Peter Eisert et al.

Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or merely exploit a single informative frame-a gap hidden by standard metrics, which reward correct predictions regardless of how they are reached. We address this from two complementary directions: first, we propose TemporalLens, a model-agnostic diagnostic framework probing temporal dependence through controlled perturbations, structured occlusions, temporal shuffling, redundancy injection, and resolution degradation, revealing whether a detector actually uses information across time. Applied to stacked-frame 2D detectors and our YOLO-3D architecture, it exposes behavioural differences invisible to mAP: stacked 2D models collapse when the target frame is removed, while spatiotemporal models recover predictions from earlier frames, a signature of real temporal reliance. Second, we detail YOLO-3D, a modular real-time spatiotemporal detector built on YOLOv8, and show that simply preserving temporal depth through the backbone is the dominant performance driver (+3.7 pp mAP@50 at 32 frames averaged across scales). Together, the diagnostics and architecture turn "does this detector reason over time?" into a measurable, actionable question.