Towards a Value-Complemented Framework for Enabling Human Monitoring in Cyber-Physical Systems

• URL: http://arxiv.org/abs/2502.07502v2
• Date: Sat, 03 May 2025 10:19:36 GMT
• Title: Towards a Value-Complemented Framework for Enabling Human Monitoring in Cyber-Physical Systems
• Authors: Zoe Pfister, Michael Vierhauser, Rebekka Wohlrab, Ruth Breu,
• Abstract summary: This research preview focuses on the importance of incorporating Privacy, Security, and Self-Direction during system design.<n>The goal is to tie functional and non-functional monitoring requirements to human values and establish traceability between values, requirements, and actors.
• Score: 5.7810359177411135
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: [Context and Motivation]: Cyber-Physical Systems (CPS) have become relevant in a wide variety of different domains, integrating hardware and software, often operating in an emerging and uncertain environment where human actors actively or passively engage with the CPS. To ensure correct and safe operation, and self-adaptation, monitors are used for collecting and analyzing diverse runtime information. [Problem]: However, monitoring humans at runtime, collecting potentially sensitive information about their actions and behavior, comes with significant ramifications that can severely hamper the successful integration of human-machine collaboration. Requirements engineering (RE) activities must integrate diverse human values, including Privacy, Security, and Self-Direction during system design, to avoid involuntary data sharing or misuse. [Principal Ideas]: In this research preview, we focus on the importance of incorporating these aspects in the RE lifecycle of eliciting and creating runtime monitors. [Contribution]: We derived an initial conceptual framework, building on the value taxonomy introduced by Schwartz and human value integrated Software Engineering by Whittle, further leveraging the concept of value tactics. The goal is to tie functional and non-functional monitoring requirements to human values and establish traceability between values, requirements, and actors. Based on this, we lay out a research roadmap guiding our ongoing work in this area.

Related papers

• InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions [58.329946838699044]
  Humans rarely plan whole-body interactions with objects at the level of explicit whole-body movements.<n>Scaling such priors is key to enabling humanoids to compose and generalize loco-manipulation skills.<n>We introduce InterPrior, a framework that learns a unified generative controller through large-scale imitation pretraining and post-training by reinforcement learning.
  arXiv  Detail & Related papers  (2026-02-05T18:59:27Z)
• Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence [55.512671026669516]
  We propose the Single-Pixel Vision-Language Model (SP-VLM), a novel framework that reimagines secure environmental monitoring.<n>It achieves intrinsic privacy-by-design by capturing human dynamics through inherently low-dimensional single-pixel modalities.<n>We show that SP-VLM can nonetheless extract meaningful behavioral semantics, enabling robust anomaly detection, people counting, and activity understanding.
  arXiv  Detail & Related papers  (2026-01-21T09:11:26Z)
• Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing [8.54096163251504]
  This paper presents its first comprehensive validation on real-world data collected by industrial partners.<n>We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment.
  arXiv  Detail & Related papers  (2025-12-10T09:33:03Z)
• HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario [63.77482302352545]
  HHI-Assist is a dataset comprising motion capture clips of human-human interactions in assistive tasks.<n>Our work has the potential to significantly enhance robotic assistance policies.
  arXiv  Detail & Related papers  (2025-09-12T09:38:17Z)
• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring [55.159556673975544]
  We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring.<n>Our approach enables robust identification and tracking of large species in their natural habitat.
  arXiv  Detail & Related papers  (2025-08-20T20:05:05Z)
• Human-Machine Collaboration and Ethical Considerations in Adaptive Cyber-Physical Systems [0.0]
  Human-Machine Teaming (HMT) represents the most advanced paradigm of human-machine collaboration.<n> achieving effective and seamless HMT in adaptive CPS is challenging.<n>This research addresses these challenges by developing novel methods and processes for integrating HMT into adaptive CPS.
  arXiv  Detail & Related papers  (2025-07-03T12:34:52Z)
• Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning [69.71072181304066]
  We introduce Perceptive Dexterous Control (PDC), a framework for vision-driven whole-body control with simulated humanoids.<n>PDC operates solely on egocentric vision for task specification, enabling object search, target placement, and skill selection through visual cues.<n>We show that training from scratch with reinforcement learning can produce emergent behaviors such as active search.
  arXiv  Detail & Related papers  (2025-05-18T07:33:31Z)
• Enabling Privacy-Aware AI-Based Ergonomic Analysis [2.4622431772551256]
  Musculoskeletal disorders (MSDs) are a leading cause of injury and productivity loss in the manufacturing industry.<n>We propose a privacy-aware ergonomic assessment framework utilizing machine learning techniques.
  arXiv  Detail & Related papers  (2025-05-12T07:52:48Z)
• HA-VLN: A Benchmark for Human-Aware Navigation in Discrete-Continuous Environments with Dynamic Multi-Human Interactions, Real-World Validation, and an Open Leaderboard [63.54109142085327]
  Vision-and-Language Navigation (VLN) systems often focus on either discrete (panoramic) or continuous (free-motion) paradigms alone. We introduce a unified Human-Aware VLN benchmark that merges these paradigms under explicit social-awareness constraints.
  arXiv  Detail & Related papers  (2025-03-18T13:05:55Z)
• A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning [67.72413262980272]
  Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear. We develop SlotMIM, a method that induces object-centric representations by introducing a semantic bottleneck. Our approach achieves significant improvements over prior work in image recognition, scene understanding, and robot learning evaluations.
  arXiv  Detail & Related papers  (2025-03-10T06:18:31Z)
• A system for objectively measuring behavior and the environment to support large-scale studies on childhood obesity [7.588188945850937]
  We present an integrated system that collects and extracts multiple behavioral and environmental indicators.<n>Our goal is to present a detailed account of the design principles, the implementation processes, and the evaluation of integrated algorithms.
  arXiv  Detail & Related papers  (2025-01-05T14:27:09Z)
• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
  ConCue is a novel approach for improving visual feature extraction in HOI detection. We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions [82.90906153293585]
  We propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects.
  arXiv  Detail & Related papers  (2022-06-25T09:55:39Z)
• Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring [0.0]
  Multitask Detection, neural Pose and Estimation Network (DSPM) We propose our Multitask Detection, neural Pose and Estimation Network (DSPM) Our architecture allows a flexible combination of the three mentioned tasks during a simple end-to-end training. We perform comprehensive evaluations on the public datasets SVIRO and TiCaM in order to demonstrate the superior performance.
  arXiv  Detail & Related papers  (2022-05-03T14:11:18Z)
• Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
  We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model. This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
  arXiv  Detail & Related papers  (2021-12-07T18:50:42Z)
• Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
  We instrument each machine by means of an external agent to generate user activity. This agent combines both deterministic and deep learning based methods to adapt to different environment. We also propose conditional text generation models to facilitate the creation of conversations and documents.
  arXiv  Detail & Related papers  (2021-11-23T10:53:29Z)
• An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments [0.0]
  This framework aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs) It can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment.
  arXiv  Detail & Related papers  (2021-06-29T17:50:54Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• Single Run Action Detector over Video Stream -- A Privacy Preserving Approach [13.247009439182769]
  This paper presents Single Run Action Detector (S-RAD) which is a real-time privacy-preserving action detector. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches.
  arXiv  Detail & Related papers  (2021-02-05T19:27:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.