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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Almassri, Ahmed Koyanagi, Ken'Ichi Wada, Chikamune Horio, Keiichi Hasan, Wan Zuha Wan Toyama Prefectural University Department of Intelligent Robotics 5180 Kurokawa Toyama Imizu Japan Kyushu Institute of Technology Department of Life Science and Systems Engineering 2-4 Hibikino Fukuoka Kitakyushu Japan Universiti Putra Malaysia Department of Electrical and Electronic Selangor 43400 Malaysia Gaza Palestine

ISBN: (纸本)9798350320831

Robotic Hand glove is one of the most commonly used technique in the rehabilitation systems. In this paper, we developed a robotic hand system with a proposed sensing mechanism-based AI algorithm, which can acquire grasping forces from human fingers. It is composed of five low-cost force sensors attached to the glove's fingertips and wireless data logger. Several experiments including grasping a plastic bottle and squeezing a tennis ball are implemented to verify the efficiency of the proposed system using the developed glove. As a result, it accurately estimates the forces applied by each finger with the aim of achieving a secure grasp comparison with conventional methods. © 2023 IEEE.

关键词： Robotic arms

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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Industrial Time Series Data Cleaning Using Generative LSTM and Adaptive Confidence Interval 3

Industrial Time Series Data Cleaning Using Generative LSTM a...

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3rd International Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2021

作者： Shi, Feng Gao, Yanjun Zhang, Zheng Jia, Haoran Xi'an Jiaotong University Institute of Robotics Intelligent Systems Xi'an China Institute of Automotive Engineering Shaan Xi Communications Technical College Xi'an China

ISBN: (纸本)9781665437189

In this paper, a method of industrial time series data cleaning using generative LSTM model and adaptive confidence interval is proposed. Firstly, the generative LSTM model is used to predict the probability distribution of industrial time series data, and then the confidence boundary of data anomalies is calculated according to the adaptive confidence interval method, so as to judge whether the data is abnormal or not. This paper uses network open data to complete data cleaning experiment, and the precision rate and recall rate of abnormal data detection are above 96%. The comparison with other anomaly detection algorithms also proves the effectiveness of the proposed algorithm in industrial time series data cleaning. © 2021 IEEE.

关键词： Open Data

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine 14

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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14th International Conference on Information and Communication systems, ICICS 2023

作者： Alkhushayni, Suboh Alomari, Zakaria Al-Zaleq, Du'a Yarmouk University Faculty of Information Technology and Computer Science Dept. Information Systems Irbid Jordan New York Institute of Technology Dept. Computer Science VancouverBC Canada Al-Ahliyya Amman University Dept. Robotics and Artificial Intelligence Engineering Amman Jordan

ISBN: (纸本)9798350307870

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution. © 2023 IEEE.

关键词： COVID-19

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A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

arXiv

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arXiv 2025年

作者： Qi, Haoxiang Yu, Zhangguo Chen, Xuechao Liu, Yaliang Yi, Chuanku Dong, Chencheng Meng, Fei Huang, Qiang The Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China The International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework. Note to Practitioners-The motivation of this paper stems from the need to improve jumping performance of humanoid robots. By comprehensively considering factors such as robot posture, centroidal angular momentum, and landing foot placement, the algorithm enhances the robot's ability to navigate complex environments. This capability is crucial for applications that require overcoming obstacles, such as in search and rescue or inspection tasks. Improved jumping ability can significantly boost environmental adaptability, allowing robots to perform effectively in diverse conditions, and it also represents an exploration of the high-dynamic motion capabilities of humanoid robots. Future research will focus on integrating visual and perceptual information to enhance decision-making. Copyright © 2025, The Authors. All rights reserve

关键词： Anthropomorphic robots

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Au-MPN@Ag Nanocomposite-Assisted Mass Spectrometry for Metabolite Detection

Au-MPN@Ag Nanocomposite-Assisted Mass Spectrometry for Metab...

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International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Qi Sang Yanyan Li Wanshan Liu Yuning Wang Kun Qian State Key Labtoratory of Systems Medicine for Cancer School of Biomedical Engineering Institute of Medical Robotics and Shanghai Academy of Experiment Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350362107

ISBN: (纸本)9798350362114

Sensitive detection of metabolites in biofluids plays an important role in disease diagnosis. Herein, we synthesized a novel noble metal based nanocomposite using metal-polyphenol network (MPN) as coordination and redox reaction. The formed Au-MPN@Ag nanocomposite exhibited excellent metabolite detection performance in laser desorption ionization mass spectrometry (LDI-MS). Notably, Au-MPN@Ag nanocomposite based LDI-MS platform achieved direct detection of metabolites in biofluids without any sample pretreatment. Our work provides a new advanced platform for metabolic analysis towards in vitro diagnostics.

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Robotic Eye-in-hand Visual Servo Axially Aligning Nasopharyngeal Swabs with the Nasal Cavity

arXiv

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arXiv 2024年

作者： Lee, Peter Q. Zelek, John S. Mombaur, Katja Systems Design Engineering University of Waterloo 200 University Avenue West Waterloo Canada Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue West Waterloo Canada Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

The nasopharyngeal (NP) swab test is a method for collecting cultures to diagnose for different types of respiratory illnesses, including COVID-19. Delegating this task to robots would be beneficial in terms of reducing infection risks and bolstering the healthcare system, but a critical component of the NP swab test is having the swab aligned properly with the nasal cavity so that it does not cause excessive discomfort or injury by traveling down the wrong passage. Existing research towards robotic NP swabbing typically assumes the patient’s head is held within a fixture. This simplifies the alignment problem, but is also dissimilar to clinical scenarios where patients are typically free-standing. Consequently, our work creates a vision-guided pipeline to allow an instrumented robot arm to properly position and orient NP swabs with respect to the nostrils of free-standing patients. The first component of the pipeline is a precomputed joint lookup table to allow the arm to meet the patient’s arbitrary position in the designated workspace, while avoiding joint limits. Our pipeline leverages semantic face models from computer vision to estimate the Euclidean pose of the face with respect to a monocular RGB-D camera placed on the end-effector. These estimates are passed into an unscented Kalman filter on manifolds state estimator and a pose based visual servo control loop to move the swab to the designated pose in front of the nostril. Our pipeline was validated with human trials, featuring a cohort of 25 participants. The system is effective, reaching the nostril for 84% of participants, and our statistical analysis did not find significant demographic biases within the cohort. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual servoing

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A Dual-Stage Attention-Based LSTM Neural Network for Tool Remaining Useful Life Prediction 3

A Dual-Stage Attention-Based LSTM Neural Network for Tool Re...

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3rd International Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2021

作者： Jia, Haoran Zhang, Zheng Gao, Yanjun Shi, Feng Institute of Robotics Intelligent Systems Xi'an Jiaotong University Xi'an China Institute of Automotive Engineering Shaan Xi Communications Technical College Xi'an China

ISBN: (纸本)9781665437189

As an indispensable part of the mechanical manufacturing system, the wear state of the tool will affect the productivity and machining quality. Therefore, it is of great significance to accurately predict its remaining useful life (RUL). This paper proposes a non-autoregressive dual-stage attention-based long-short term memory network (DA-LSTM) for tool RUL prediction. The dual-stage attention mechanism of the proposed method can adaptively assign attention weights to the input features and the context vectors output by the LSTM encoder, thereby improving the performance of the model without manual feature selection. Validation experiments are conducted on a real public dataset, and the experimental results show that the method proposed in this paper has better performance in short-term and medium-term RUL prediction tasks. © 2021 IEEE.

关键词： Long short-term memory

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N Observation Photon Counting Imaging With Preprocessing

N Observation Photon Counting Imaging With Preprocessing

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International Joint Conference on Computer Science and Software engineering (JCSSE)

作者： Gilsu Yeo Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka City Fukuoka School of ICT Robotics and Mechanical Engineering IITC Hankyoung National University Anseong Kyonggi-do Republic of Korea

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

In this paper, we explain our method to enhance the conventional integral imaging method. The photon-counting imaging is used to obtain the image including objects or scene under photon-lacked environments. And the conventional $N$ observation photon counting imaging obtains three-dimensional (3D) image by repeating the estimation to obtain more accurate information of object or scene than conventional photon-counting imaging under almost photon-lacked environments. To enhance the visual quality and information of 3D objects in the conventional $\mathrm{N}$ observation photon counting imaging, the volumetric computational reconstruction (VCR) is utilized as preprocessing and combined $N$ observation photon counting integral imaging in this paper.

关键词： Measurement Visualization Three-dimensional displays Accuracy Imaging Lakes Video recording

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A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces

A Slices Perspective for Incremental Nonparametric Inference...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Moshe Shienman Ohad Levy-Or Michael Kaess Vadim Indelman Technion Autonomous Systems Program (TASP) Technion - Israel Institute of Technology Haifa Israel Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages slices from highdimensional surfaces to efficiently approximate posterior distributions of any shape. Unlike many existing graph-based methods, our slices perspective eliminates the need for additional intermediate reconstructions, maintaining a more accurate representation of posterior distributions. Additionally, we propose a novel heuristic to balance between accuracy and efficiency, enabling real-time operation in nonparametric scenarios. In empirical evaluations on synthetic and real-world datasets, our slices approach consistently outperforms other state-of-the-art methods. It demonstrates superior accuracy and achieves a significant reduction in computational complexity, often by an order of magnitude.

关键词： Surface reconstruction Accuracy Shape Probabilistic logic Real-time systems Iterative methods Computational complexity Intelligent robots

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