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共 2094 条记录，以下是211-220 订阅

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Human Activity Recognition with IMU and Vital Signs Feature fusion 28th

Human Activity Recognition with IMU and Vital Signs Feature ...

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28th International conference on MultiMedia Modeling (MMM)

作者： Xefteris, Vasileios-Rafail Tsanousa, Athina Mavropoulos, Thanassis Meditskos, Georgios Vrochidis, Stefanos Kompatsiaris, Ioannis Informat Technol Inst Ctr Res & Technol Hellas 6th Km Charilaou Thermi Thessaloniki 57001 Greece Aristotle Univ Thessaloniki Sch Informat Thessaloniki Greece

ISBN: (纸本)9783030983581;9783030983574

Combining data from different sources into an integrated view is a recent trend taking advantage of the Internet of Things (IoT) evolution over the last years. The fusion of different modalities has applications in various fields, including healthcare and security systems. Human activity recognition (HAR) is among the most common applications of a healthcare or eldercare system. Inertial measurement unit (IMU) wearable sensors, like accelerometers and gyroscopes, are often utilized for HAR applications. In this paper, we investigate the performance of wearable IMU sensors along with vital signs sensors for HAR. A massive feature extraction, including both time and frequency domain features and transitional features for the vital signs, along with a feature selection method were performed. The classification algorithms and different early and late fusion methods were applied to a public dataset. Experimental results revealed that both IMU and vital signs achieve reasonable HAR accuracy and Fl-score among all the classes. Feature selection significantly reduced the number of features from both IMU and vital signs features while also improved the classification accuracy. The rest of the early and late level fusion methods also performed better than each modality alone, reaching an accuracy level of up to 95.32%.

关键词： Human activity recognition Wearable sensors Vital signals sensor fusion Feature selection

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Field Experiments with an Automated Utility Platform for Transportation and Work Processes

Field Experiments with an Automated Utility Platform for Tra...

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IEEE International conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Edlinger, Raimund Dumberger, Stefan Froschauer, Roman Pointner, Wolfgang Zeilinger, Marcel Nuechter, Andreas Univ Appl Sci Upper Austria Smart Automat & Robot Wels Austria AIT Austrian Inst Technol Assist & Autonomous Syst Vienna Austria Julius Maximilians Univ Wurzburg Informat Robot & Telemat 7 Wurzburg Germany

ISBN: (纸本)9781665482172

The automation of transport and work tasks in various application domains continues to be a major challenge. The proposed paper presents key technologies within a process from sensing to reasoning and includes: (i) understanding the scene and its relations through new approaches in object classification and pose estimation based on machine learning, as well as methods for reliable detection of objects and object features;(ii) reliable and safe outdoor localization and mapping based on multi-modal approaches and innovative camera-based methods. The fusion of different sensor data is used for reliable obstacle detection and collision avoidance. The investigation of suitable system architectures, data processing platforms and communication mechanisms taking into account safety requirements is a prerequisite for the operation in a safety-critical environment. The paper provides a proof of concept for an automated platform to demonstrate and evaluate application scenario provided by the Linz Airport in the form of an automated transport process for air freight between a terminal and the aircraft.

关键词： Localization Mapping and Navigation applications of Autonomous Intelligent Robots Autonomous Robotic Systems Robotic Simulation sensors and sensor Integration

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Unilateral Lower Extremity Exoskeleton Utilizing sensor fusion algorithms

Unilateral Lower Extremity Exoskeleton Utilizing Sensor Fusi...

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IEEE Research and applications of Photonics In Defense conference (RAPID)

作者： R. Dizor A. Raj T. Stewart B. Gonzalez G. Smith Z. Carter B. Domingues J. Newton Department Intelligent Systems and Robotics University of West Florida/ Pensacola Florida USA Florida Institute of Human and Machine Cognition Pensacola Florida USA Embry-Riddle Aeronautical University Daytona Beach Florida USA

Electromyography (EMG) and Inertial Measurement (IMU) sensors are increasingly associated with exoskeletons and prosthetics. Coordination and synchronization of these sensors with a sensor fusion algorithm modifying an array of multiple armband sensors allows sensing the user's intended movement and controlling a unilateral lower extremity exoskeleton.

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Improved RGB-D Indoor Semantic Segmentation using Cascaded Loss fusion

Improved RGB-D Indoor Semantic Segmentation using Cascaded L...

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IEEE International conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Sonali Patil Anita Sellent Andreas Gerndt Georgia Albuquerque Institute for Software Technology German Aerospace Center (DLR) Braunschweig Germany Dept. of Geoinformatics and Surveying Mainz University of Applied Science Mainz Germany

Semantic segmentation of images promises numerous benefits for augmented reality applications. However, in such applications typical scenes are challenging for current segmentation algorithms due to high variability in object appearances and distribution. We propose a new cascaded loss fusion strategy to improve the training schedule of state-of-the-art realtime RGB-D semantic segmentation architectures. We employ methods developed in the context of multi-task learning to solve the multiclass and multi-loss learning problems in semantic segmentation. Through our quantitative evaluation on the NYUv2 [3] and SUNRGB-D [4] benchmark datasets, we show improvement over the state-of-the-art approaches. Furthermore, our approach improves results qualitatively on both the benchmark datasets as well as on our own recordings of some scenarios that are typical for head-mounted cameras.

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Arastronaut: An Open Source UWB/IMU Hardware and Software for Indoor Positioning

Arastronaut: An Open Source UWB/IMU Hardware and Software fo...

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RSI/ISM International conference on Robotics and Mechatronics (ICROM)

作者： M. Vakilil A. S. Mirjalili M. R. Dindarloo A. Sharifi H. D. Taghirad Applied Robotics and AI Solutions (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Arastronaut is an open-source 1 1 https://***/mehtivakili/Arastronaut platform integrating hardware and software components to achieve high-precision indoor positioning. At its core, the system leverages ultra-wideband (UWB) technology for ranging, while incorporating an inertial measurement unit (IMU) to enable future sensor fusion capabilities. The system utilizes readily available, costeffective components including an ESP32 microcontroller, a UWB transceiver, and an IMU. The architecture prioritizes real-time operation and modularity for flexible tag/anchor deployment. Currently, the software implements two way ranging (TWR) with time division multiple access (TDMA) for precise distance measurement and robust time synchronization. Furthermore, it incorporates sensor-specific calibration algorithms for enhanced accuracy. An elegant graphical user interface (GUI) has been developed to facilitate user accessibility, streamline calibration processes, and enable seamless data fusion for sensor fusion applications. Experimental results validate the performance of individual UWB and IMU modules, demonstrating the platform's potential for centimeter-level indoor localization and paving the way for future integrated sensor fusion implementations.

关键词： Time division multiple access Accuracy sensor fusion Robot sensing systems Distance measurement Software Transceivers Calibration Synchronization Ultra wideband technology

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Trust-Based Data fusion and Machine Learning for Underwater sensor Networks

Trust-Based Data Fusion and Machine Learning for Underwater ...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Anita Kanavalli Shilpa Shashikant Chaudhari Bharathi S Department of CSE Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Underwater Wireless sensor Networks (UWSNs) are essential for a wide range of marine applications, from environmental monitoring to underwater exploration. However, significant obstacles such signal attenuation, multipath propagation, and sensor node mobility make it difficult to sustain flawless communication in dynamic underwater environments. In UWSNs, handover prediction becomes essential for maintaining continuous communication and maximizing the effectiveness of data transfer. This research presents a novel method for precise handover prediction in UWSNs by utilizing machine learning algorithms and real-time maritime data. The system attempts to predict Energy Trust, Environment Trust, and Communication Trust events by examining environmental parameters and past handover events, improving communication efficiency and reliability in dynamic underwater environments.

关键词： Wireless sensor networks Technological innovation Machine learning algorithms Data integration Handover Data models Real-time systems Robustness Environmental monitoring Ensemble learning

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Enhancing Indoor Mobile Robot Localization through the Integration of Multi-sensor fusion algorithms

Enhancing Indoor Mobile Robot Localization through the Integ...

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Robotics, Engineering, Science, and Technology (RESTCON), International conference on

作者： Rut Yatigul Noppadol Pudchuen Aran Blattler Wisanu Jitviriya Department of Production and Robotics Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Instrumentation and Electronics Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

This paper presents an innovative approach that combines visual odometry, Inertial Measurement Unit (IMU), and wheel odometry, using the Extended Kalman Filter and Unscented Kalman Filter to enhance mobile robot localization and mapping. The primary goal is to improve localization accuracy and robustness while providing a cost-effective alternative to traditional SLAM methods that rely on expensive LIDAR and RGBD camera systems. By integrating visual odometry, IMU data, and wheel odometry our method not only enhances precision and robustness of the mobile robot localization system but also reduces the financial burden associated with high-end sensor hardware. This fusion creates a reliable solution, particularly suited for resource-constrained environments. Our research contributes to the democratization of SLAM technologies, making them more accessible to a wider range of applications. The results presented in this report showcase the potential of our approach. This paper highlights the pivotal role of multi-sensor fusion framework which significantly enhances localization accuracy and robustness.

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Distributed fusion Estimation for Multi-sensor with Bounded Noise

Distributed Fusion Estimation for Multi-Sensor with Bounded ...

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International conference on Mechanical and Electronics Engineering (ICMEE)

作者： Shuqi Wei Jiaoru Huang Electronic Information Engineering Xi'an Technological University Xi'an China

ISBN: (数字)9798350307146

ISBN: (纸本)9798350307153

In practical applications, sensors may encounter disturbances that result in the measurement of outliers, leading to abnormal data. Therefore, when performing distributed fusion estimation for time-varying discrete systems with multiple sensors, it is assumed that the noise is bounded and contains outliers, and the goal is to estimate the true state of the system. The improved set-membership filtering algorithm is used to filter the bounded noise with outliers. The design of local filters involves transmitting the local estimates from each sensor to the fusion center. Considering the integrity and accuracy of information, an improved Minkowski sum algorithm is adopted to achieve global estimation. Finally, the effectiveness of the proposed multi-sensor robust distributed fusion is verified through a simulation example.

关键词： Noise Estimation Data integration Filtering algorithms sensor fusion Information filters sensor systems

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A Finite- Time Complementary Filter for Attitude Estimation Using Inertial sensors

A Finite- Time Complementary Filter for Attitude Estimation ...

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IEEE International conference on Unmanned Systems (ICUS)

作者： Linan Wang Guanghui Wen Xiaojian Yi Xinhe Wang Geng Liu Department of Systems Science Southeast University Nanjing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Cyber Science and Engineering Southeast University Nanjing China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

This paper presents a novel finite-time complementary filter specifically designed for attitude and gyroscope bias estimation. The key contributions of this filter include ensuring rapid convergence to the actual attitude within a finite time, significantly improving performance over traditional filters that only guarantee asymptotic stability. The filter achieves high accuracy by effectively integrating data from multiple sensors, such as gyroscopes, accelerometers, and magnetometers, through advanced sensor fusion algorithms and noise suppression techniques. Additionally, simulation tests confirm the filter's robust resistance to noise, highlighting its potential to enhance the performance of attitude estimation systems across various applications by providing faster, more accurate, and reliable estimates.

关键词： Accuracy Magnetometers Magnetic separation Magnetic sensors Noise reduction Noise Filtering algorithms sensor fusion Gyroscopes Convergence

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The Future of Driving: A Review on the Combination of Artificial Intelligence and Autonomous Vehicles

The Future of Driving: A Review on the Combination of Artifi...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Nikita Zade Mayur Gawande Prateek Verma Swapnil Gundewar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and Machine Learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.

关键词： Ethics Machine learning algorithms Reviews Navigation Decision making sensor fusion Safety Artificial intelligence Socioeconomics Automotive engineering

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