arXiv subjects

Stefan Reitmann

Publications and source records attributed to Stefan Reitmann.

6 recordsLinked to original sources

arXiv2026

Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments

This paper presents an Edge AI-based system for detecting sleep and wake states in non-stationary mobile environments using resource-constrained embedded hardware. Conventional approaches relying on accelerometer-based activity metrics are highly susceptible to motion and vibration artifacts and are limited by strict compute and energy budgets of wearable and IoT devices. To address these challenges, a multimodal pipeline is designed and implemented on an ESP32-S3 microcontroller. The system combines inertial sensing for head movement analysis and visual pose classification. A dual-core architecture with FreeRTOS enables parallel execution of real-time data acquisition and on-device inference. Sleep detection follows a two-stage strategy: low-movement detection over a temporal window, followed by visual validation of poses. Experimental results show accuracies of 96.5% for motion-based detection and 89% for pose classification, yielding robust binary sleep-wake classification. Field tests confirmed feasibility in representative mobile scenarios. The results demonstrate that privacy-preserving, local sleep detection is achievable on edge hardware through careful co-design, while highlighting limitations in sensing intrusiveness, dataset scale, and system integration.

cs.LG↗

arXiv2026

Towards Intent-Aware Human-Robot Teaming: A Platform for Search-and-Rescue Operations

We investigate the challenges of enabling effective collaboration between human operators and heterogeneous autonomous agents in complex, dynamic environments by developing an interaction platform that allows study of operator behavior and supports intent inference and decision-making using state-of-the-art frameworks. We demonstrate the extent to which the operator's perception, decisions, and actions could be supported by autonomous systems during search-and-rescue operations with our platform.

cs.RO↗

arXiv2026

DCI: Dependency Confidence Index for Assessing Open-Source Dependency Trustworthiness

Selecting trustworthy open source software dependencies remains a major challenge in software supply chain security. We present the Dependency Confidence Index (DCI), a composite formative index that combines nine empirically weighted trust factors into a single normalized composite score for dependency selection. DCI's trust factors combine insights from a systematic literature review and an exploratory Analytic Hierarchy Process (AHP) survey of ten software developers, highlighting security, source code quality, and project health as the most influential dimensions. Following Goal-Question-Metric methodology, we implemented 12 automated measurements using SonarQube, GitHub APIs, and OpenSSF Scorecard data, deployed in a containerized evaluation platform. We conducted a pilot evaluation of the normalized DCI on 92 popular PyPI packages, observing moderate agreement with OpenSSF Scorecard scores and perfect test--retest reliability. Analysis reveals process-based factors (dependency management, CI) dominate scores on high-quality packages, while security metrics saturate---suggesting DCI's complementary role to existing tools. Our publicly available implementation provides a foundation for open source software trustworthiness research and practical dependency auditing.

cs.SE↗

arXiv2026

Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edge

Deploying three-dimensional deep learning frameworks to low-power embedded processors is bottlenecked by the unstructured nature of spatial data and the resource-intensive distance sorting algorithms often used before neural network inference. To address this gap, this paper presents a hardware-constrained workflow optimized for native execution on the Raspberry Pi 5. To account for the reality gap between noiseless, clean computer-aided design (CAD) datasets and real-world sensor data, we use physics-based simulation to construct a synthetic LiDAR dataset. Cross-dataset evaluations demonstrate a substantial drop in classification accuracy when networks trained on clean CAD data are evaluated on synthetic LiDAR sensor data, highlighting the critical need for sensor-aware training. To address the latency bottleneck of traditional geometric preprocessing on edge CPUs, we integrate an isolated, feature-driven Critical Points Layer (CPL) as a frontend filter. Our results show that the pretrained CPL deterministically compresses raw 1024-point clouds to a subset of 40 to 60 unique coordinates. When profiled on the ARM Cortex-A76 processor, the complete pipeline achieves an inference throughput of approximately 50 FPS while maintaining an instance classification accuracy of 88.36%, demonstrating the viability of deterministic real-time 3D perception at the edge.

cs.LG↗

arXiv2025

Impact of Gaze-Based Interaction and Augmentation on Human-Robot Collaboration in Critical Tasks

We present a user study analyzing head-gaze-based robot control and foveated visual augmentation in a simulated search-and-rescue task. Results show that foveated augmentation significantly improves task performance, reduces cognitive load by 38%, and shortens task time by over 60%. Head-gaze patterns analysed over both the entire task duration and shorter time segments show that near and far attention capture is essential to better understand user intention in critical scenarios. Our findings highlight the potential of foveation as an augmentation technique and the need to further study gaze measures to leverage them during critical tasks.

cs.RO↗

arXiv2025

Injecting Conflict Situations in Autonomous Driving Simulation using CARLA

Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This paper presents a set of exemplary conflict scenarios in CARLA that arise in shared autonomy settings, where both AVs and human drivers must navigate complex traffic environments. We explore various conflict situations, focusing on the impact of driver behavior and decision-making processes on overall traffic safety and efficiency. We build a simple extendable toolkit for situation awareness research, in which the implemented conflicts can be demonstrated.

cs.HC↗