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International Conference on Acoustics, speech, and signal Processing (ICAssP)

作者： Tin-Han Chi Kai-Chun Liu Chia-Yeh Hsieh Yu Tsao Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems" using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms).

关键词： Computational modeling system performance Aging Transformers Reliability Fall detection Usability

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PREFALLKD: PRE-IMPACT FALL DETECTION VIA CNN-VIT KNOWLEDGE DIsTILLATION

arXiv

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arXiv 2023年

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

关键词： Fall detection

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

arXiv

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arXiv 2023年

作者： Liu, Chien-Pin Li, Ju-Hsuan Chu, En-Ping Hsieh, Chia-Yeh Liu, Kai-Chun Chan, Chia-Tai Tsao, Yu Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei112 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei242062 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115 Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie." Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LsTM) for FD. Copyright © 2023, The Authors. All rights reserved.

关键词： Fall detection

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Deep Learning-based Fall Detection Algorithm Using Ensemble ...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Chien-Pin Liu Ju-Hsuan Li En-Ping Chu Chia-Yeh Hsieh Kai-Chun Liu Chia-Tai Chan Yu Tsao Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a “long-lie.” Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LsTM) for FD.

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Enhancing Automated Microorganism Classification: A study on the Influence of Compression, Non-Uniform scaling, and Data Augmentation Using Deep Learning

SSRN

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ssRN 2024年

作者： Boukouvalas, Dimitria Theophanis Bissaco, Márcia A.s. Dellê, Humberto Deana, Alessandro M. Belan, Peterson A. de Araújo, sidnei Alves Universidade Nove de Julho Informatics and Knowledge Management Graduate Program Rua Vergueiro 235/249 SP São Paulo01504-001 Brazil Universidade de Mogi das Cruzes Biomedical Engineering Graduate Program Av. Dr. Cândido Xavier de Almeida e Souza 200 Mogi das Cruzes SP São Paulo08780-911 Brazil Universidade Nove de Julho Medicine Graduate Program Rua Vergueiro 235/249 SP São Paulo01504-001 Brazil

With the advent of "Laboratory 4.0", a concept that represents the recent technological breakthroughs in laboratory automation, there is an increasing demand for solutions that can automate, standardize, and enhance the accuracy and speed of microorganism classification. One promising approach is the use of convolutional neural networks (CNNs), a powerful deep learning technique that has shown significant results in biomedical image analysis. However, its application in microorganism classification is still nascent mainly due to the unavailability of extensive datasets, which is a basic requirement to ensure generalization ability in learning CNNs. This study aims to fill a significant gap in the literature by exploring the influence of factors such as image compression, non-uniform scaling, and data augmentation on the performance of CNNs, specifically when using small image datasets of microorganisms. We focus on two CNN architectures, AlexNet and DenseNet-121, which have shown promising results in previous microorganism classification studies. Our experiments reveal that certain factors like the type of data compression and non-uniform scaling applied to the images can negatively impact the performance of CNNs. On the other hand, data augmentation techniques such as mirroring, rotation, and noise addition can enhance their performance. By utilizing training datasets and optimized parameters based on our findings, we were able to achieve classification accuracies of 98.61% and 99.82% with AlexNet and DenseNet-121, respectively, surpassing the results reported in current state-of-the-art studies. This research has significant implications for Laboratory 4.0, potentially reducing dataset preparation and CNN training time, lowering computational costs, and improving the consistency and accuracy of microorganism classification. It paves the way for more efficient and accurate microorganism classification methods in automated laboratories. © 2024, The Authors. All righ

关键词： Classification (of information)

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Centrifugal spinning of Porous Polylactic Acid Fibers by Centrifugal spinning with Phase separation for Oil Removal Application

SSRN

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ssRN 2024年

作者： Negoro, Masaki Doan, Hoan Ngoc Vo, Phu Phong Khuat, Khanh Van Thi Kinashi, Kenji sakai, Wataru Tsutsumi, Naoto Master’s Program of Innovative Materials Kyoto Institute of Technology Matsugasaki Sakyo Kyoto606-8585 Japan Tissue Engineering and Regenerative Medicine Laboratory School of Biomedical Engineering International University Vietnam National University Ho Chi Minh City Viet Nam Faculty of Chemistry University of Science Ho Chi Minh City Viet Nam Doctor’s Program of Materials Chemistry Graduate School of Science and Technology Kyoto Institute of Technology Matsugasaki Sakyo Kyoto606-8585 Japan Faculty of Materials Science and Engineering Kyoto Institute of Technology Matsugasaki Sakyo Kyoto606-8585 Japan

Developing biodegradable oil absorption fibrous materials is essential because discarding conventional separation materials could cause secondary pollution due to their nondegradable properties. In this study, highly hydrophobic and superoleophilic porous polylactic acid (PLA) fibers were prepared using centrifugal spinning combined with nonsolvent-induced phase separation. The porous structure of PLA fibers was controlled by the ratio between good solvent and nonsolvent in the spinning solutions. The morphology and physical properties of the obtained PLA fibers were investigated using various measurement techniques. The PLA fibrous membrane prepared from a PLA solution containing chloroform/dimethylformamide (80/20 w/w) indicated not only a high water contact angle but also a high oil sorption value. The porous PLA fibers are a potential candidate for use as green materials for environmental applications. © 2024, The Authors. All rights reserved.

关键词： spinning (fibers)

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Computational prediction and validation of specific EmbR binding site on PknH

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synthetic and systems Biotechnology 2021年第4期6卷 429-436页

作者： Insung Na Huanqin Dai Hantian Li Anvita Gupta David Kreda Powell Zhang Xiangyin Chen Lixin Zhang Gil Alterovitz Computational Health Informatics Program Boston Children’s Hospital/Harvard Medical SchoolBostonMA 02115USA Department of Medicine Brigham and Women’s Hospital/Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Mycology Institute of MicrobiologyBeijing100101 China Department of Computer Sciences Stanford UniversityStanford CA 94305USA Center for Biomedical Informatics Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Bioreactor Engineering East China University of Science and TechnologyShanghaiChina Department of Biology The Johns Hopkins UniversityBaltimoreMD 21218USA Lexington High School BostonMA 02115USA

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not ***(EMB),one of the well-known first-line drugs in tuberculosis treatment is,unfortunately,not free from drug resistance *** studies have shown that some genetic mutations in Mycobacterium tuberculosis(Mtb)EmbR,and EmbC/A/B genes cause EMB ***-PknH pair controls embC/A/B operon,which encodes EmbC/A/B genes,and EMB interacts with EmbA/B ***,the EmbR binding site on PknH was *** conducted molecular simulation on the EmbR-peptides binding structures and discovered phosphorylated PknH 273-280(N′-HEALs^(P)DPD-C′)makesβstrand with the EmbR FHA domain,asβ-MoRF(MoRF;molecular recognition feature)does at its binding *** bond number analysis also supported the peptides’β-MoRF forming activity at the EmbR FHA ***,we discovered that previously known phosphorylation residues might have their chronological order according to the phosphorylation *** discovery validated that Mtb PknH 273-280(N′-HEALsDPD-C′)has reliable EmbR binding *** approach is revolutionary in the computer-aided drug discovery field,because it is the first trial to discover the protein-protein interaction site,and find binding partner in nature from this site.

关键词： Disorder-to-order transition Protein intrinsic disorder Binding site prediction Drug resistance Molecular simulation

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Multi-Task Learning U-Net for Functional shoulder sub-Task segmentation

Multi-Task Learning U-Net for Functional Shoulder Sub-Task S...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology society (EMBC)

作者： En-Ping Chu Kai-Chun Liu Chia-Yeh Hsieh Chih-Ya Chang Yu Tsao Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei Taiwan Research Center for Information Technology Innovation (CITI) at Academia Sinica Taipei Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Department of Physical Medicine and Rehabilitation School of Medicine National Defense Medical Center Tri-Service General Hospital Taipei Taiwan

The assessment of a frozen shoulder (Fs) is critical for evaluating outcomes and medical treatment. Analysis of functional shoulder sub-tasks provides more crucial information, but current manual labeling methods are time-consuming and prone to errors. To address this challenge, we propose a deep multi-task learning (MTL) U-Net to provide an automatic and reliable functional shoulder sub-task segmentation (sTs) tool for clinical evaluation in Fs. The proposed approach contains the main task of sTs and the auxiliary task of transition point detection (TPD). For the main sTs task, a U-Net architecture including an encoder-decoder with skip connection is presented to perform shoulder sub-task classification for each time point. The auxiliary TPD task uses lightweight convolutional neural networks architecture to detect the boundary between shoulder sub-tasks. A shared structure is implemented between two tasks and their objective functions of them are optimized jointly. The fine-grained transition-related information from the auxiliary TPD task is expected to help the main sTs task better detect boundaries between functional shoulder sub-tasks. We conduct the experiments using wearable inertial measurement units to record 815 shoulder task sequences collected from 20 healthy subjects and 43 patients with Fs. The experimental results present that the deep MTL U-Net can achieve superior performance compared to using single-task models. It shows the effectiveness of the proposed method for functional shoulder sTs. The code has been made publicly available at https://***/RobinChu9890/*** Relevance— This work provides an automatic and reliable functional shoulder sub-task segmentation tool for clinical evaluation in frozen shoulder.

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Genomic data processing with GenomeFlow

arXiv

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arXiv 2025年

作者： Park, Junseok Maury, Eduardo A. Oh, Changhoon shin, Donghoon Denisko, Danielle Lee, Eunjung Alice Division of Genetics and Genomics Boston Children’s Hospital 3 Blackfan Circle BostonMA United States Department of Pediatrics Harvard Medical School 25 Shattuck St BostonMA United States Bioinformatics Integrative Genomics Program Harvard/MIT MD-PHD Program Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Graduate School of Information Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul Korea Republic of Department of Human Centered Design and Engineering University of Washington SeattleWA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public repositories. On-demand infrastructure services on cloud computing platforms enable the processing of such large-scale genomic sequence data in distributed processing environments with a significant reduction in analysis time. However, parallel processing on cloud computing platforms presents many challenges to researchers, even skillful bioinformaticians. In particular, it is difficult to design a computing architecture optimized to reduce the cost of computing and disk storage as genomic data analysis pipelines often employ many heterogeneous tools with different resource requirements. To address these issues, we developed GenomeFlow, a tool for automated development of computing architecture and resource optimization on Google Cloud Platform, which allows users to process a large number of samples at minimal cost. We outline multiple use cases of GenomeFlow demonstrating its utility to significantly reduce computing time and cost associated with analyzing genomic and transcriptomic data from hundreds to tens of thousands of samples from several consortia. Here, we describe a step-by-step protocol on how to use GenomeFlow for a common genomic data processing task. We introduce this example protocol geared toward a bioinformatician with little experience in cloud computing. © 2025, CC BY.

关键词： Cloud platforms

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Prediction of anterior cruciate ligament injury from MRI using deep learning

Prediction of anterior cruciate ligament injury from MRI usi...

引用

International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Truong, Nguyen Khanh Hung Nguyen, Thuan Phuoc Kha, Quang Hien Le, Ngoc Hoang Le, Van Tuan Cao, Thi Luu, Ho Thanh Lam Le, Nguyen Quoc Khanh Kang, Jiunn-Horng International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Orthopedic and Trauma Cho Ray Hospital Ho Chi Minh city Viet Nam Mechatronics Engineering Ho Chi Minh City University of Technology Ho Chi Minh city Viet Nam Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Orthopedic and Rehabilitation Department University of Medicine and Pharmacy at Ho Chi Minh city Ho Chi Minh city Viet Nam Children's Hospital 2 Ho Chi Minh City Viet Nam Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Knee injury emerges as one of the most common diseases, causing dislocation of knee joints, immobility, etc., in which the anterior cruciate ligament (ACL) injury is the most common one. The development of various artificial intelligence (AI) frameworks gained enormous attention in many areas, including injury prediction and health management via medical image analysis. The objective of the current study is to focus on a comprehensive high accurate prediction of ACL injury based on MRI medical images, and also demonstrate the ability of AI in practical and outline conceptual prediction and diagnosis frameworks for other types of knee injuries in the future. Our dataset comprised of knee MRI reports from Cho Ray Hospital, Vietnam which are composed of ACL and non-ACL injury patients. The MRI images were used as supporting data in the deep learning classification model with DenseNet-121 algorithm. The successful establishment of an ACL injury diagnosis model from MRI will pave the way for us to develop more diagnostic models of other injuries in the body as well as the prediction of bone diseases. © 2021 sPIE.

关键词： Magnetic resonance imaging

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