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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ticllacuri, Victor Mio, Renato Biomedical Engineering Program PUCP-UPCH Lima Peru Pontificia Universidad Catolica del Peru Laboratory of Biomechanics and Applied Robotics Lima Peru Universidad Peruana Cayetano Heredia Laboratory of Biomedical Engineering Lima Peru University of Bayreuth Digital Health with a Focus on Data Science Germany

ISBN: (纸本)9798350371499

Treatments of disuse-induced muscle atrophy entail unmet clinical needs due to the lack of medical devices capable of mimicking physicians manual therapies. Therefore, in this paper we develop and model a wearable soft pneumatic elastomeric actuator to perform deep cyclic compression stimuli on human soft tissues for muscle rehabilitation by mechanotherapy. Static and dynamic characterization of the prototype demonstrate a 2.5 mm active deformation at 100 kPa with 600 mm3/s and a 5 Hz bandwidth. We estimate the transfer function of the experimentally acquired pressure, flow and deformation signals, processed by a Gaussian kernel-based approach. Our mathematical model accurately describes the actuator behavior and enables to extract its mechanical parameters. Then, through computational simulations, we illustrate its efficacy in emulating multiple complex bio-inspired movements. Our proposed methodology aims to improve the control efficiency in wearable soft robotics for muscle atrophy treatment. © 2024 IEEE.

关键词： Pneumatic actuators

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Machine Learning Algorithms for Natural Language Processing Tasks: A Case of COVID-19 Twitter data (Thailand)

Machine Learning Algorithms for Natural Language Processing ...

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Kularbphettong, Kunyanuth Vichivanives, Rujijan Kanjanaprakarn, Pannawat Chutrtong, Jaruwan Ruksuntorn, Nareenart Suan Sunandha Rajabhat University Computer Science Program Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Science and Technology Bangkok Thailand Suan Sunandha Rajabhat University Robotics Engineering Program Faculty of Industrial Technology Bangkok Thailand

ISBN: (纸本)9798350320282

This paper presents the use of natural language processing for the problem of information extraction and sentiment analysis. The dataset is from Twitter that has the information of people mentioning about COVID-19, this study has two tasks: (i) classification approach for information extraction task and (ii) deep learning approach for sentiment analysis task. In information extraction task, the data was gathered from twitter that related to COVID-19 information, and the sequence labelling method applied to classify text before giving it to classification algorithms (K-NN, Naïve Bayes, Decision Tree, Random Forest, and SVM). In sentiment analysis task, data was classified by convert the word into index and using word embedding, then to process deep learning algorithm (Bi-directional GRU). The accuracy of two tasks are 98% and 79% respectively. © 2022 IEEE.

关键词： COVID-19

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K-Radar: 4D radar object detection for autonomous driving in various weather conditions 22

K-Radar: 4D radar object detection for autonomous driving in...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Dong-Hee Paek Seung-Hyun Kong Kevin Tirta Wijaya CCS Graduate School of Mobility KAIST Robotics Program KAIST

ISBN: (纸本)9781713871088

Unlike RGB cameras that use visible light bands (384~769 THz) and Lidars that use infrared bands (361~331 THz), Radars use relatively longer wavelength radio bands (77~81 GHz), resulting in robust measurements in adverse weathers. Unfortunately, existing Radar datasets only contain a relatively small number of samples compared to the existing camera and Lidar datasets. This may hinder the development of sophisticated data-driven deep learning techniques for Radar-based perception. Moreover, most of the existing Radar datasets only provide 3D Radar tensor (3DRT) data that contain power measurements along the Doppler, range, and azimuth dimensions. As there is no elevation information, it is challenging to estimate the 3D bounding box of an object from 3DRT. In this work, we introduce KAIST-Radar (K-Radar), a novel large-scale object detection dataset and benchmark that contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions, together with carefully annotated 3D bounding box labels of objects on the roads. K-Radar includes challenging driving conditions such as adverse weathers (fog, rain, and snow) on various road structures (urban, suburban roads, alleyways, and highways). In addition to the 4DRT, we provide auxiliary measurements from carefully calibrated high-resolution Lidars, surround stereo cameras, and RTK-GPS. We also provide 4DRT-based object detection baseline neural networks (baseline NNs) and show that the height information is crucial for 3D object detection. And by comparing the baseline NN with a similarly-structured Lidar-based neural network, we demonstrate that 4D Radar is a more robust sensor for adverse weather conditions.

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RelMobNet: End-to-End Relative Camera Pose Estimation Using a Robust Two-Stage Training 17th

RelMobNet: End-to-End Relative Camera Pose Estimation Using ...

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17th European Conference on Computer Vision, ECCV 2022

作者： Rajendran, Praveen Kumar Mishra, Sumit Vecchietti, Luiz Felipe Har, Dongsoo Division of Future Vehicle KAIST Daejeon Korea Republic of The Robotics Program KAIST Daejeon Korea Republic of Data Science Group Institute for Basic Science Daejeon Korea Republic of The CCS Graduate School of Mobility KAIST Daejeon Korea Republic of

ISBN: (纸本)9783031250743

Relative camera pose estimation, i.e. estimating the translation and rotation vectors using a pair of images taken in different locations, is an important part of systems in augmented reality and robotics. In this paper, we present an end-to-end relative camera pose estimation network using a siamese architecture that is independent of camera parameters. The network is trained using the Cambridge Landmarks data with four individual scene datasets and a dataset combining the four scenes. To improve generalization, we propose a novel two-stage training that alleviates the need of a hyperparameter to balance the translation and rotation loss scale. The proposed method is compared with one-stage training CNN-based methods such as RPNet and RCPNet and demonstrate that the proposed model improves translation vector estimation by 16.11%, 28.88%, and 52.27% on the Kings College, Old Hospital, and St Marys Church scenes, respectively. For proving texture invariance, we investigate the generalization of the proposed method augmenting the datasets to different scene styles, as ablation studies, using generative adversarial networks. Also, we present a qualitative assessment of epipolar lines of our network predictions and ground truth poses. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN)

Development of Detection Lung Disease Stand Alone System Bas...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Edgar Caesar Liewarnata Yosica Mariana Sofyan Tan Suryadiputra Liawatimena Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification.

关键词： Training Accuracy Tuberculosis Pulmonary diseases Lungs Stethoscope Recording Convolutional neural networks Mel frequency cepstral coefficient Testing

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Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection

arXiv

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arXiv 2023年

作者： Paek, Dong-Hee Kong, Seung-Hyun Wijaya, Kevin Tirta CCS Graduate School of Mobility KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

Recent works have shown the superior robustness of four-dimensional (4D) Radar-based three-dimensional (3D) object detection in adverse weather conditions. However, processing 4D Radar data remains a challenge due to the large data size, which require substantial amount of memory for computing and storage. In previous work, an online density reduction is performed on the 4D Radar Tensor (4DRT) to reduce the data size, in which the density reduction level is chosen arbitrarily. However, the impact of density reduction on the detection performance and memory consumption remains largely unknown. In this paper, we aim to address this issue by conducting extensive hyperparamter tuning on the density reduction level. Experimental results show that increasing the density level from 0.01% to 50% of the original 4DRT density level proportionally improves the detection performance, at a cost of memory consumption. However, when the density level is increased beyond 5%, only the memory consumption increases, while the detection performance oscillates below the peak point. In addition to the optimized density hyperparameter, we also introduce 4D Sparse Radar Tensor (4DSRT), a new representation for 4D Radar data with offline density reduction, leading to a significantly reduced raw data size. An optimized development kit for training the neural networks is also provided, which along with the utilization of 4DSRT, improves training speed by a factor of 17.1 compared to the state-of-the-art 4DRT-based neural networks. All codes are available at: https://***/kaist-avelab/K-Radar. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection

Enhanced K-Radar: Optimal Density Reduction to Improve Detec...

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IEEE Symposium on Intelligent Vehicle

作者： Dong-Hee Paek Seung-Hyun Kong Kevin Tirta Wijaya CCS Graduate School of Mobility KAIST Daejeon Republic of Korea Robotics Program KAIST Daejeon Republic of Korea

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Multitask Lower Limb Classification System for Dorsiflexion Rehabilitation by Using Electroencephalography and Functional Electrical Stimulation

Multitask Lower Limb Classification System for Dorsiflexion ...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Gonzalez-Cely A. X. André Felipe Oliveira De Azevedo Dantas Caroline Cunha Do Espirito Santo Teodiano F. Bastos-Filho Denis Delisle-Rodriguez Robotics and Assistive Technology Laboratory Federal University of Espirito Santo Vitoria Brazil Postgraduate Program in Neuroengineering Santos Dumont Institute Macaiba Brazil

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Providing alternatives for lower limb moment control in post-stroke or post-Spinal Cord Injury (SCI) patients who have suffered a severe neural impairment many years ago can further improve the effect of rehabilitation. This work proposes a multitask system for dorsiflexion of the right, left, and both feet through Electroencephalography and Functional Electrical Stimulation (FES). A Butterworth bandpass filter between 8 and 30 Hz is implemented, and subsequently, three strategies are developed by first considering the power spectral density features in mu, low-beta, and high-beta bands responsible for motor task execution. Moreover, the second strategy uses Common Spatial Patterns filters, and the third strategy uses Riemannian Geometry (RG) to extract features. Four distinct classifiers were explored: k-nearest neighbors, support vector machine, Linear Discriminant Analysis (LDA), and decision tree. Based on Accuracy (ACC), True Positive Rate (TPR), and False Positive Rate (FPR) were evaluated the classifiers and the strategies implemented by achieving an ACC of 0.79, a TPR of 0.89 and a FPR of 0.05 employing RG + LDA. The implemented methodology allows the feasibility of a Human-Machine Interface (HMI) which implements a lower-limb MI task recognition system to be used in rehabilitation. Future work will implement the recognition system in real-time to users move the desired limb by activating a FES that generates contraction.

关键词： Support vector machines Neuroplasticity Filters Muscles Feature extraction Iron Real-time systems

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Human Following Robot Based on Object Detection for Aerospace Application 1

Human Following Robot Based on Object Detection for Aerospac...

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1st International Conference of Adisutjipto on Aerospace Electrical Engineering and Informatics, ICAAEEI 2024

作者： Sumari, Arwin Datumava Wahvudi Aryandika, Ahnaf Bagus Purnami, Ndaru Atmi Dermawan, Denny Syamsiana, Ika Noer State Polytechnic of Malang Cognitive Artificial Intelligence Research Group Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Aerosp. Digit. Syst. Microprocessor Microcontroller and Programmable Logic Devices Research Group Yogyakarta Indonesia

ISBN: (纸本)9798331507930

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications. © 2024 IEEE.

关键词： Travel time

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Study on Optimal Machine Learning Approaches for Kidney Failure Detection System Based on Ammonia Level in the Mount 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Phandinata, Nicholas Puji, Muhammad Nurul Astuti, Winda Andriatin, Yuli Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Nursery Department Cilacap Regional General Hospital Gatot Subroto No.28 Central Jawa Cilacap53223 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Patients with kidney failure can emit bad breath with a certain level of ammonia content. The interpretation of ammonia levels in patients with renal failure was identified in this work. This research used the Chronic Kidney Disease dataset from UCI Machine Learning Repository. The dataset was processed first to get the value of eGFR (estimated Glomerular Filtration Rate) and ppb (parts per billion) of ammonia. Based on the eGFR value, the severity of kidney failure was divided into 5 categories, namely normal (Stage 1), mild (stage 2), moderate (stage 3), severe (stage 4) and failure (stage 5). The values of eGFR features are used as input for the machine learning technique in order to predict the level of kidney failure. Four different types of machine learning techniques, namely Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-nearest neighbors (KNN), are applied and compared. The last process was to identify kidney failure using the K-Nearest Neighbors (KNN) method based on eGFR and ppb of ammonia dataset. AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 89.9% and 95.65% for training and testing accuracy, respectfully. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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