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A Sensing Platform to Monitor Sleep Efficiency 11th

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A Sensing Platform to Monitor Sleep Efficiency

11th Italian Forum on Ambient Assisted Living, ForItAAL 2020

作者： Crivello, Antonino La Rosa, Davide Wilhelm, Elisabeth Palumbo, Filippo 1 Via G. Moruzzi Pisa56124 Italy Department of Health Sciences and Technology Istitute of Robotics and Intelligent Systems Sensory Motor Systems Lab ETH Zurich Zurich8092 Switzerland Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9783031088377

Sleep plays a fundamental role in the human life. Sleep research is mainly focused on the understanding of the sleep patterns, stages and duration. An accurate sleep monitoring can detect early signs of sleep deprivation and insomnia consequentially implementing mechanisms for preventing and overcoming these problems. Recently, sleep monitoring has been achieved using wearable technologies, able to analyse also the body movements, but old people can encounter some difficulties in using and maintaining these devices. In this paper, we propose an unobtrusive sensing platform able to analyze body movements, infer sleep duration and awakenings occurred along the night, and evaluating the sleep efficiency index. To prove the feasibility of the suggested method we did a pilot trial in which several healthy users have been involved. The sensors were installed within the bed and, on each day, each user was administered with the Groningen Sleep Quality Scale questionnaire to evaluate the user’s perceived sleep quality. Finally, we show potential correlation between a perceived evaluation with an objective index as the sleep efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sleep research

Environment-Driven Online LiDAR-Camera Extrinsic Calibration

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

A Dataset on Human-Cobot Collaboration for Action Recognition in Manufacturing Assembly

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A Dataset on Human-Cobot Collaboration for Action Recognitio...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： L. Romeo M. V. Maselli M. García Domínguez R. Marani M. Lavit Nicora G. Cicirelli M. Malosio T. D’Orazio Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA) National Research Council (CNR) of Italy Department of Mathematics and Computer Science Universidad de La Rioja Logroño Spain

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a dataset on Human-cobot collaboration for Action Recognition in Manufacturing Assembly (HARMA 3 ). It is a collection of RGB frames, Depth maps, RGB-to-depth-Aligned (RGB-A) frames and Skeleton data relative to actions performed by different subjects in collaboration with a cobot for building an Epicyclic Gear Train (EGT). In particular, 27 subjects executed several trials of the assembly task, which consisted of 7 actions. Data were collected in a laboratory scenario using two Microsoft ® Azure Kinect cameras positioned in frontal and lateral positions. The dataset represents a good foundation for developing and testing advanced action recognition as well as action segmentation systems with far-reaching implications beyond human-cobot collaboration. Further potential applications include computer Vision, Machine Learning, and Smart Manufacturing. Preliminary experiments for action segmentation by applying a state-of-the-art method on features extracted from RGB and skeletal data are presented in this paper, showing high-performance rates.

关键词： Gears Collaboration Machine learning Feature extraction Skeleton Information technology Assembly Testing Videos Smart manufacturing

An Explicit Nonlinear Model for Large Spatial Deflections of Symmetric Slender Beams

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SSRN

SSRN 2024年

作者： Chen, Yuhan Yao, Shilong Liu, Li Meng, Max Q.-H. Institute of Robotics and Intelligent Systems Dalian University of Technology Liaoning Dalian116024 China Department of Electronic and Electrical Engineering Southern University of Science and Technology Guangzhou Shenzhen518055 China The Chinese University of Hong Kong Hong Kong

Flexible slender beams are commonly used in compliant mechanisms and continuum robots. However, the modeling of these beams can be complicated due to the geometric nonlinearity becoming significant at large elastic deﬂections. This paper presents an explicit nonlinear model for large spatial deflections of a slender beam with uniform, symmetrical sections subjected to general end-loading. The elongation, bending, torsion, and shear deformations of the beams are modeled based on Timoshenko’s assumptions and Cosserat rod theory. Subsequently, the nonlinear governing differential equations for the beam are derived from the quaternion representation of the rotation matrix. The explicit load-displacement relations of the beam are obtained using the improved Adomian decomposition method. This method is superior to the classical Adomian decomposition method in terms of convergence rate and domain. The convergence and superiority of the method are also rigorously demonstrated. Simulations are provided to verify the two- and three-dimensional deﬂections of beams. Real-world experiments have also been performed to validate our method's effectiveness with two different beam configurations. The results indicate that the proposed method accurately estimates large spatial deﬂections of ﬂexible beams. © 2024, The Authors. All rights reserved.

关键词： Shear deformation

A Bottom-Up Method for Pose Detection of Multiple People on Real-Time Video 1

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15th International Conference on Pattern Recognition and Information Processing, PRIP 2021

作者： Leunikau, Aliaksandr Nedzved, Alexander Belotserkovsky, Alexei Sholtanyuk, Stanislav Minsk Belarus Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus Department of Intelligent Information Systems United Institute of Informatics Problems of NAS Belarus Minsk Belarus Belarusian State University Minsk Belarus

ISBN: (数字)9783030988838

ISBN: (纸本)9783030988821

关键词： Image recognition

DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework 39

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DCHM: Dynamic Collaboration of Heterogeneous Models Through ...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Key Laboratory of Commercial Data Security Protection and Intelligent Governance Beijing Technology and Business University Beijing China Department of Computer and Information Science Faculty of Science and Technology University of Macau Taipa Macao Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa Macao

ISBN: (纸本)157735897X

Solutions to time-varying problems are crucial for research areas such as predicting changes in human body shape over time. While recurrent neural networks have made significant advancements in this field, their reliance on centralized processing has led to challenges such as model silos and data isolation. In response, distributed AI systems like federated learning have emerged to facilitate dynamic collaboration among models;however, they still depend on central coordinators, which pose risks to system security and efficiency. Moreover, traditional federated learning primarily supports homogeneous models and lacks effective strategies for the interaction of heterogeneous models. To address these limitations, we propose a novel method called Dynamic Collaboration of Heterogeneous Models (DCHM), based on Isomerism Learning, which leverages a consortium blockchain network to enhance model credibility and facilitate coordination among heterogeneous models. Additionally, we introduce a Distributed Hierarchical Aggregation (DHA) algorithm that enables permissioned nodes within each group to aggregate local model results and share them for standardized processing. After several iterative cycles, these nodes perform secondary integration of local results to produce global outcomes. Experimental results demonstrate that DCHM effectively analyzes the temporal variability of body shape changes with high efficiency. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe

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arXiv 2024年

作者： Xu, Ningyu Zhang, Qi Zhang, Menghan Qian, Peng Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Research Institute of Intelligent Complex Systems Fudan University China Department of Psychology Harvard University Hong Kong

Probing and enhancing large language models’ reasoning capacity remains a crucial open question. Here we re-purpose the reverse dictionary task as a case study to probe LLMs’ capacity for conceptual inference. We use in-context learning to guide the models to generate the term for an object concept implied in a linguistic description. Models robustly achieve high accuracy in this task, and their representation space encodes information about object categories and fine-grained features. Further experiments suggest that the conceptual inference ability as probed by the reverse-dictionary task predicts model’s general reasoning performance across multiple benchmarks, despite similar syntactic generalization behaviors across models. Explorative analyses suggest that prompting LLMs with description⇒word examples may induce generalization beyond surface-level differences in task construals and facilitate models on broader commonsense reasoning problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection

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arXiv 2024年

作者： Svystun, Serhii Melnychenko, Oleksandr Radiuk, Pavlo Savenko, Oleg Sachenko, Anatoliy Lysyi, Andrii Department of Computer Engineering and Information Systems Khmelnytskyi National University Khmelnytskyi29016 Ukraine Department of Computer Science Khmelnytskyi National University Khmelnytskyi29016 Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil46020 Ukraine Department of Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom26600 Poland

The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model’s accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments

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arXiv 2024年

作者： Jiao, Jianhao Geng, Ruoyu Li, Yuanhang Xin, Ren Yang, Bowen Wu, Jin Wang, Lujia Liu, Ming Fan, Rui Kanoulas, Dimitrios Department of Computer Science University College London LondonWC1E 6BT United Kingdom Nansha Guangdong Guangzhou511400 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Nansha Guangdong Guangzhou511400 China College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems The Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this paper, we introduce an online metric-semantic mapping system that utilizes LiDAR-Visual-Inertial sensing to generate a global metric-semantic mesh map of large-scale outdoor environments. Leveraging GPU acceleration, our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale. Furthermore, we seamlessly integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment. Through extensive experiments conducted on both publicly available and self-collected datasets comprising 24 sequences, we demonstrate the effectiveness of our mapping and navigation methodologies. Code has been publicly released: https://***/gogojjh/cobra. Note to Practitioners—This paper tackles the challenge of autonomous navigation for mobile robots in complex, unstructured environments with rich semantic elements. Traditional navigation relies on geometric analysis and manual annotations, struggling to differentiate similar structures like roads and sidewalks. We propose an online mapping system that creates a global metric-semantic mesh map for large-scale outdoor environments, utilizing GPU acceleration for speed and overcoming the limitations of existing real-time semantic mapping methods, which are generally confined to indoor settings. Our map integrates into a real-world navigation system, proven effective in localization and terrain assessment through experiments with both public and proprietary datasets. Future work will focus on integrating kernel-based

关键词： Mesh generation