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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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BART LAB AI Elderly Support Robot for Enhanced Biomechanical Assistance in Sit-to-Stand and Walking Activities

BART LAB AI Elderly Support Robot for Enhanced Biomechanical...

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Rehabilitation engineering and Assistive Technology (i-CREATe), International Convention on

作者： Jackrit Suthakorn Branesh M. Pillai Bibhu Sharma Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

The increasing elderly population underscores the pressing need for technological solutions that enhance Quality of Life (QoL) by facilitating independent living. Mobility, encompassing essential movements such as sit-to-stand, stand-to-sit, and walking, becomes increasingly challenging with age, making it a critical aspect of independence in the elderly. This research aims to develop a BART LAB AI-Elderly Supporting Robot, designed to assist elderly individuals in sit-to-stand and walking activities, thereby improving their mobility and independence. The design and control mechanisms of the robot are based on detailed biomechanical modeling and optimized to align with the natural movement patterns of elderly individuals. The iterative design process involved biomechanical analysis, system optimization, and the subsequent development and validation of the robot. The optimization process utilized biomechanical data to tailor the kinematic and kinetic features of the robot, ensuring that the support provided is both effective and intuitive for the user. The validation results demonstrated that the robot significantly improved the mobility of elderly users while respecting their physiological limits, thereby enhancing their ability to live independently and safely. This development has substantial implications for elderly care by offering a robotic assistant that improves mobility and promotes safer and more independent living for the elderly.

关键词： Legged locomotion Biomechanics Technological innovation Pressing Reliability engineering Physiology Iterative methods Older adults Robots Optimization

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Advancing Mobility in Stair Climbing with BART LAB's Intelligent Wheelchair: A Deep Learning Approach to Pose Estimation

Advancing Mobility in Stair Climbing with BART LAB's Intelli...

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Rehabilitation engineering and Assistive Technology (i-CREATe), International Convention on

作者： Branesh M. Pillai Dileep Sivaraman Songpol Ongwattanakul Jackrit Suthakorn Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

This study addresses the critical urban mobility challenges faced by individuals with lower-limb impairments, particularly the limitations of conventional wheelchairs in navigating stairs of varying dimensions. Conventional wheelchairs struggle with these challenges, particularly those with non-uniform stair dimensions, which impede urban accessibility. To overcome these limitations, we introduce a semi-autonomous tracked stair-climbing wheelchair (SCW) that features an innovative kinematic mechanism that seamlessly switches locomotion modes and autonomously adjusts the wheelchair's pose to adapt to diverse terrains. The research focuses on enhancing pose estimation capabilities during stair climbing through a novel deep-learning approach. By integrating a depth camera with an advanced kinematic model, a sophisticated algorithm capable of precise stair width estimation and pose adjustment has been developed. Experimental results confirm the system's effectiveness, achieving a Mean Absolute Error (MAE) of 0.05 meters and a Root Mean Squared Error (RMSE) of 0.07 meters in height measurement, with high precision (0.92) and recall (0.89) rates in identifying stair components. These findings demonstrate the system's capability to overcome accessibility and safety challenges, offering a technical stride towards improved inclusivity and independence in urban environments.

关键词： Meters Navigation Wheelchairs Semantic segmentation Pose estimation Urban areas Kinematics Stairs Robustness Safety

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Improving Intersession Reproducibility for Forearm Ultrasound based Hand Gesture Classification through an Incremental Learning Approach

Improving Intersession Reproducibility for Forearm Ultrasoun...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Jack Rothenberg Keshav Bimbraw Haichong Zhang Robotics and Biomedical Engineering Worcester Polytechnic Institute Worcester USA Robotics Engineering Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound images of the forearm can be used to classify hand gestures towards developing human machine interfaces. In our previous work, we have demonstrated gesture classification using ultrasound on a single subject without removing the probe before evaluation. This has limitations in usage as once the probe is removed and replaced, the accuracy declines since the classifier performance is sensitive to the probe location on the arm. In this paper, we propose training a model on multiple data collection sessions to create a generalized model, utilizing incremental learning through fine tuning. Ultrasound data was acquired for 5 hand gestures within a session (without removing and putting the probe back on) and across sessions. A convolutional neural network (CNN) with 5 cascaded convolution layers was used for this study. A pre-trained CNN was fine tuned with the convolution blocks acting as a feature extractor, and the parameters of the remaining layers updated in an incremental fashion. Fine tuning was done using different session splits within a session and between multiple sessions. We found that incremental fine tuning can help enhance classification accuracy with more fine tuning sessions. After 2 fine tuning sessions for each experiment, we found an approximate 10% increase in classification accuracy. This work demonstrates that incremental learning through fine tuning on ultrasound based hand gesture classification can be used improves accuracy while saving storage, processing power, and time. It can be expanded to generalize between multiple subjects and towards developing personalized wearable devices.

关键词： Hands Training Ultrasonic imaging Accuracy Incremental learning Feature extraction Data models Convolutional neural networks Probes Tuning

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Hand Gesture Classification Based on Forearm Ultrasound Video Snippets Using 3D Convolutional Neural Networks

Hand Gesture Classification Based on Forearm Ultrasound Vide...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Keshav Bimbraw Ankit Talele Haichong Zhang Robotics Engineering Worcester Polytechnic Institute Worcester USA Robotics and Biomedical Engineering Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound based hand movement estimation is a crucial area of research with applications in human-machine interaction. Forearm ultrasound offers detailed information about muscle morphology changes during hand movement which can be used to estimate hand gestures. Previous work has focused on analyzing 2-Dimensional (2D) ultrasound image frames using techniques such as convolutional neural networks (CNNs). However, such 2D techniques do not capture temporal features from segments of ultrasound data corresponding to continuous hand movements. This study uses 3D CNN based techniques to capture spatio-temporal patterns within ultrasound video segments for gesture recognition. We compared the performance of a 2D convolution-based network with (2+1)D convolution-based, 3D convolution-based, and our proposed network. Our methodology enhanced the gesture classification accuracy to 98.8 ± 0.9%, from 96.5 ± 2.3% compared to a network trained with 2D convolution layers. These results demonstrate the advantages of using ultrasound video snippets for improving hand gesture classification performance.

关键词： Hands Human computer interaction Image segmentation Solid modeling Ultrasonic imaging Three-dimensional displays Accuracy Gesture recognition Spatiotemporal phenomena Convolutional neural networks

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Instantaneous Center of Rotation Trajectory Tracking of A Novel Five-bar Robotic Exoskeleton for Knee Rehabilitation 30

Instantaneous Center of Rotation Trajectory Tracking of A No...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Abdullah, Ahmad Jayachandran, Jaideep Thattamparambil Quan Xie, Sheng Liu, Aiqin University of Leeds School of Biomedical Sciences Leeds United Kingdom University of Leeds Institute of Robotics and Autonomous Systems School of Electronic and Electrical Engineering Leeds United Kingdom

ISBN: (纸本)9798350391916

Knee Exoskeletons provide a way to restore the quality of life in people with knee disorders or after injury. One of the major limitations of these exoskeletons is to mimic the natural polycentric knee joint motion. The design and performance analysis of a novel five-bar mechanism is proposed in this study which can be used in a knee exoskeleton. The purpose of this study was to analyze the instantaneous center of rotation trajectory of the five-bar mechanism mimicking the natural knee center of rotation trajectory during sit to stand motion and compare it with similar existing mechanisms. The position analysis of the mechanism in a simplified configuration and the actual hardware configuration was performed for analyzing the center of rotation trajectory, both theoretically and using a dedicated tool respectively. The motion and the center of rotation trajectory of five-bar mechanism was validated, and the results of the study are presented which show the successful tracking of the reference Instantaneous center of rotation with an error as low as 0.14mm for the simplified configuration and 0.24mm for the actual configuration. These results were then compared with different bar mechanisms showing that our five-bar mechanism had better tracking performance among the available rigid mechanisms. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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Augmented Grasps: Supernumerary Robotic Fingers and Their Practical Uses 9th

Augmented Grasps: Supernumerary Robotic Fingers and Their Pr...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Maguina, Alyssa N. Mio, Renato Laboratory of Biomechanics and Applied Robotics Pontifical Catholic University of Peru Lima Peru Biomedical Engineering Program PUCP-UPCH Universidad Peruana Cayetano Heredia Lima Peru

ISBN: (纸本)9783031494062

Research on supernumerary robotic fingers has grown in recent years, both for augmentation and assistance purposes. Unlike other wearable robotics, these devices do not replace a missing finger or assist finger movement, but provide an additional one to extend the workspace and enhance grasp capabilities in healthy patients. In addition, patients with physical disabilities can use them as assistive devices for basic grasping in activities of daily living. While research in this field moves forward consistently, a classification of the grasps performed with extra fingers has not yet been proposed. In the literature, we find a number of papers that focus on improving finger control but do not present which grips are similar to a natural five finger grasp. In this paper, a brief classification of supernumerary grasps will be made based on the most common ones developed in the literature. In addition, the usefulness of these additional robotic fingers will be described in order to encourage more researchers to opt for the development and improvement of these devices. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wearable technology

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ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross- and Self-Attention Fusion 22

ACSGRegNet: A Deep Learning-based Framework for Unsupervised...

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4th International Conference on Intelligent Medicine and Health, ICIMH 2022

作者： Gao, Xiaoru Zheng, Guoyan Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781450397087

Registration plays an important role in medical image analysis. Deep learning-based methods have been studied for medical image registration, which leverage convolutional neural networks (CNNs) for efficiently regressing a dense deformation field from a pair of images. However, CNNs are limited in its ability to extract semantically meaningful intra- and inter-image spatial correspondences, which are of importance for accurate image registration. This study proposes a novel end-to-end deep learning-based framework for unsupervised affine and diffeomorphic deformable registration, referred as ACSGRegNet, which integrates a cross-attention module for establishing inter-image feature correspondences and a self-attention module for intra-image anatomical structures aware. Both attention modules are built on transformer encoders. The output from each attention module is respectively fed to a decoder to generate a velocity field. We further introduce a gated fusion module to fuse both velocity fields. The fused velocity field is then integrated to a dense deformation field. Extensive experiments are conducted on lumbar spine CT images. Once the model is trained, pairs of unseen lumbar vertebrae can be registered in one shot. Evaluated on 450 pairs of vertebral CT data, our method achieved an average Dice of 0.963 and an average distance error of 0.321mm, which are better than the state-of-the-art (SOTA). © 2022 ACM.

关键词： Image registration

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The role of artificial intelligence and IoT in prediction of earthquakes:Review

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Artificial Intelligence in Geosciences 2024年第1期5卷 154-172页

作者： Joshua Pwavodi Abdullahi Umar Ibrahim Pwadubashiyi Coston Pwavodi Fadi Al-Turjman Ali Mohand-Said Ecole et Observatoire des Sciences de La Terre ITES UMR7063-CNRS/Universit’e de Strasbourg5 rue Rene Descartes67084StrasbourgFrance Research Center for Science Technology and Engineering(BILTEM)Near East University99138NicosiaTRNCMersin 10Turkey Department of Biomedical Engineering Near East UniversityMersin 10Nicosia99010Turkey Department of Biomedical Engineering Faculty of EngineeringCyprus International UniversityHaspolatNorth Cyprus via Mersin 10Nicosia99258Turkey Artificial Intelligence Engineering Department AI and Robotics InstituteNear East UniversityMersin99138Turkey Research Center for AI and IoT Faculty of EngineeringUniversity of KyreniaMersin 99138Turkey

Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment,lives,and *** has been an increasing interest in the prediction of earthquakes and in gaining a comprehensive understanding of the mechanisms that underlie their generation,yet earthquakes are the least predictable natural *** data,global positioning system,interferometry synthetic aperture radar(InSAR),and seismometers such as microelectromechanical system,seismometers,ocean bottom seismometers,and distributed acoustic sensing systems have all been used to predict earthquakes with a high degree of *** advances in seismic wave recording,storage,and analysis,earthquake time,location,and magnitude prediction remain *** the other hand,new developments in artificial intelligence(AI)and the Internet of Things(IoT)have shown promising potential to deliver more insights and ***,this article reviewed the use of AI-driven Models and IoT-based technologies for the prediction of earthquakes,the limitations of current approaches,and open research *** review discusses earthquake prediction setbacks due to insufficient data,inconsistencies,diversity of earthquake precursor signals,and the earth’s geophysical ***,this study examines potential approaches or solutions that scientists can employ to address the challenges they face in earthquake *** analysis is based on the successful application of AI and IoT in other fields.

关键词： Earthquakes Seismicity Artificial intelligence Internet of things Prediction

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SelfMedHPM: Self Pre-Training with Hard Patches Mining Masked Autoencoders for Medical Image Segmentation 22

SelfMedHPM: Self Pre-Training with Hard Patches Mining Maske...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Lv, Yunhao Chen, Lingyu Wang, Jian Li, Yangxi Chen, Fang Nanjing University of Aeronautics and Astronautics Department of Computer Science and Engineering Nanjing China School of Biomedical Engineering Institute of Medical Robotics Shanghai JiaoTong University Shanghai 200240 China

ISBN: (纸本)9798331520526

In recent years, deep learning methods such as convolutional neural network (CNN) and transformers have made significant progress in CT multi-organ segmentation. However, CT multi-organ segmentation methods based on masked image modeling (MIM) are very limited. There are already methods using MAE for CT multi-organ segmentation task, we believe that the existing methods do not identify the most difficult areas to reconstruct. To this end, we propose a MIM self-training framework with hard patches mining masked autoencoders for CT multi-organ segmentation tasks (self-MedHPM). The method performs ViT self-pretraining on the training set of the target data and introduces an auxiliary loss predictor, which first predicts the patch loss and determines the location of the next mask. SelfMedHPM implementation is better than various competitive methods in abdominal CT multi-organ segmentation and body CT multi-organ segmentation. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for abdomen mult-organ segmentation and the SinoMed Whole Body (SMWB) dataset for body multi-organ segmentation tasks. © 2025 IEEE.

关键词： Hard patches mining Masked autoencoders Masked image modeling Medical image segmentation

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