IEEE Journal of Biomedical and Health Informatics最新文献_第7页

Poincaré Image Analysis of Short-Term Electrocardiogram for Detecting Atrial Fibrillation. 短期心电图对房颤检测的poincarcars图像分析。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-21 DOI: 10.1109/JBHI.2025.3562778

Md Mayenul Islam, Mohammod Abdul Motin, Sumaiya Kabir, Dinesh Kumar

{"title":"Poincaré Image Analysis of Short-Term Electrocardiogram for Detecting Atrial Fibrillation.","authors":"Md Mayenul Islam, Mohammod Abdul Motin, Sumaiya Kabir, Dinesh Kumar","doi":"10.1109/JBHI.2025.3562778","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3562778","url":null,"abstract":"Atrial fibrillation (AF) is a heart rhythm disorder and is associated with the risk of stroke and heart failure. Early detection of AF is crucial but challenging due to its asymptomatic nature and similarity to other ectopic beats, such as premature atrial contractions (PACs) and premature ventricular contractions (PVCs). This article presents a novel Poincaré image-domain feature-based automated AF screening model in the presence of PACs/PVCs using 10-second single-lead electrocardiogram (ECG) signals. The model proposes a hybrid approach that integrates a radial basis function-based support vector machine classifier, optimized via grid search, with a rule-based decision criterion. A set of 84 Poincaré image features is extracted and reduced to a set of four features through the minimum redundancy maximum relevance selection approach and then fed into the classifier. Additionally, rules based on P-wave information and dRR distribution patterns are incorporated to enable a more distinct separation of PACs/PVCs from AF. The model was validated using eight datasets comprising recordings from 25,776 subjects. Both 5-fold cross-validation and leave-one-dataset-out validation were performed using 2,06,367 segments: 1,12,591 normal, 9,485 PACs/PVCs, and 84,291 AF segments. The accuracy ranges were 96.35% to 99.40% and 96.48% to 99.33% for 5-fold cross-validation and leave-one-dataset-out validation, respectively, with balanced sensitivity and specificity across all datasets. The model's superior performance across diverse data demonstrates its robustness and suitability for real-world application, supporting its potential in computerized assessment of short-term ECGs to detect AF.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting Trusted Execution Environments and Distributed Computation for Genomic Association Tests. 利用可信执行环境和分布式计算进行基因组关联测试。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-18 DOI: 10.1109/JBHI.2025.3562364

Claudia V Brito, Pedro G Ferreira, Joao T Paulo

引用次数: 0

Interval-Shared Information Integration and False-Negative Association Reduction in Multi-Source MiRNA-Disease Association Prediction. 多源mirna -疾病关联预测的区间共享信息整合和假阴性关联减少

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-18 DOI: 10.1109/JBHI.2025.3562617

Qinghang Cui, Honglie Guo, Yueyi Cai, Yu Fei, Shunfang Wang

{"title":"Interval-Shared Information Integration and False-Negative Association Reduction in Multi-Source MiRNA-Disease Association Prediction.","authors":"Qinghang Cui, Honglie Guo, Yueyi Cai, Yu Fei, Shunfang Wang","doi":"10.1109/JBHI.2025.3562617","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3562617","url":null,"abstract":"Numerous studies have demonstrated that microRNAs (miRNAs) play crucial roles in the development and progression of various diseases, making the identification of miRNA-disease association (MDA) essential for understanding human disease etiology. While several computational models have been developed to predict MDAs, challenges persist-particularly the limited consideration of information interactions among multi-source similarities and the presence of \"false-negative\" associations in the original topology. To address these issues, we propose ISFNMDA, a model designed to infer potential MDAs by leveraging multi-view collaborative learning for feature extraction and optimizing association topology through graph structure momentum contrastive learning. Specifically, multi-source similarities of miRNAs and diseases are mapped into a unified feature space via encoders. The Pearson correlation coefficient is employed to derive pairwise constraints between nodes, facilitating information interactions and constructing interval-shared information constraints. Subsequently, an inference graph learner models the representations to generate an inferred graph topology. By maximizing mutual information between the inferred topology and the original \"false-negative\" associations through momentum contrastive learning, the model effectively reduces spurious correlations. The final comprehensive representations and optimized graph structure are then used to predict potential MDAs. Experimental results demonstrate that ISFNMDA outperforms existing methods, and case studies further validate its predictive capability.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PKAN: Leveraging Kolmogorov-Arnold Networks and Multi-modal Learning for Peptide Prediction with Advanced Language Models. PKAN：利用Kolmogorov-Arnold网络和多模态学习与高级语言模型进行肽预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-17 DOI: 10.1109/JBHI.2025.3561846

Li Wang, Xiangzheng Fu, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng, Yiping Liu

{"title":"PKAN: Leveraging Kolmogorov-Arnold Networks and Multi-modal Learning for Peptide Prediction with Advanced Language Models.","authors":"Li Wang, Xiangzheng Fu, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng, Yiping Liu","doi":"10.1109/JBHI.2025.3561846","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3561846","url":null,"abstract":"Peptides can offer highly specific biological activities, serving as essential mediators of intercellular signaling, which are critical for advancing precision medicine and drug development. Their primary structure can be depicted either as an amino acid sequence or as a chemical molecules consisting of atoms and chemical bonds. Large language models (LLMs) hold the potential to thoroughly elucidate the intricate intrinsic properties of peptides. Here we present the Peptide Kolmogorov-Arnold Network (PKAN), a framework leveraging multi-modal representations inspired by advanced language models for peptide activity and functionality prediction. Comparative experiments across tasks show that PKAN outperforms state-of-the-art models while maintaining a streamlined design with superior predictive capabilities. The multi-modal feature importance scoring, anchored in global structures and the significant marginal impacts of derived features on the model, coupled with intricate symbolic regression of specific activation functions, further demonstrates the robustness and precision of the PKAN framework in identifying and elucidating key determinants of peptide functionality. This work provides scientific evidence for investigating the complex mechanisms of peptide materials and supports the progression of peptide language paradigms in biology.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Drug Synergy Combination: Integrating Graph Transformers and BiLSTM for Accurate Drug Synergy Prediction. 增强药物协同组合：整合图变换和BiLSTM进行药物协同准确预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-17 DOI: 10.1109/JBHI.2025.3561887

Bin Sun, Haoze Du, Shumei Hou, Qingkai Hu, Xiaoxiao Pang, Dongqing Wei, Xianfang Wang

{"title":"Enhancing Drug Synergy Combination: Integrating Graph Transformers and BiLSTM for Accurate Drug Synergy Prediction.","authors":"Bin Sun, Haoze Du, Shumei Hou, Qingkai Hu, Xiaoxiao Pang, Dongqing Wei, Xianfang Wang","doi":"10.1109/JBHI.2025.3561887","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3561887","url":null,"abstract":"Combination therapy of drugs showed significant potential in treating complex diseases by overcoming drug resistance and improving therapeutic efficacy. However, due to the rapid increase in the number of available drugs, the cost and time required for experimentally screening synergistic drug combinations became increasingly burdensome. In this work, we proposed a novel drug synergy prediction model called GraphTranSynergy, which utilized graph transformer and BiLSTM to capture the molecular structure of drugs and gene expression features of cell lines. GraphTranSynergy extracted graphical features of drug pairs through the graph transformer module and integrated information from the BiLSTM module to extract useful features from gene expression profiles of cell lines. The final prediction of drug synergy was made through a fully connected neural network. Our model achieved AUC and PRAUC scores of 0.94, outperforming most existing models. Independent test results demonstrated that GraphTranSynergy exhibited superior generalization ability on the AstraZeneca dataset, particularly excelling in ACC and TPR metrics. Through a series of experiments and analyses, our model not only improved prediction accuracy but also demonstrated advantages in biological interpretability. The GraphTranSynergy code can be accessed at https://github.com/DreamAI-mastersun/GraphTranSynergy.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deciphering Muscular Dynamics: A Dual-Attention Framework for Predicting Muscle Contraction from Activation Patterns. 解读肌肉动力学：从激活模式预测肌肉收缩的双注意框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-17 DOI: 10.1109/JBHI.2025.3562072

Bangyu Lan, Gijs Krijnen, Stefano Stramigioli, Kenan Niu

{"title":"Deciphering Muscular Dynamics: A Dual-Attention Framework for Predicting Muscle Contraction from Activation Patterns.","authors":"Bangyu Lan, Gijs Krijnen, Stefano Stramigioli, Kenan Niu","doi":"10.1109/JBHI.2025.3562072","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3562072","url":null,"abstract":"Quantitatively deciphering the relationship between muscle activation and thickness deformation is essential for diagnosing muscle-related diseases and monitoring muscle health (e.g., Facioscapulohumeral Dystrophy). Despite the potential of ultrasound (US) imaging and sensing to measure changes in muscle thickness during movements, it remains challenging to make a fully portable device, considering the wiring and data collection. On the other hand, surface electromyography (sEMG) can record muscle bioelectrical signals and measure muscle activations, offering a unique perspective that correlates with underlying changes in muscle thickness. This paper introduces a deep-learning-based approach that used sEMG signals to infer muscle deformation. Using a hierarchical combination of self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data, eliminating the dependency on applying ultrasound imaging techniques. The experimental results on six healthy subjects indicated that our approach could accurately predict muscle excursion with an average precision of 0.923$pm$0.900mm, showing benefits in measuring muscle deformation only with a sEMG device. This technique facilitates real-time portable muscle health monitoring by sEMG to provide bioelectrical signals and biomechanical information. It indicates the great potential of using this technique in clinical diagnostics, sports science, and rehabilitation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MIT-SAM: Medical Image-Text SAM with Mutually Enhanced Heterogeneous Features Fusion for Medical Image Segmentation. MIT-SAM：基于相互增强异构特征融合的医学图像-文本SAM。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-16 DOI: 10.1109/JBHI.2025.3561425

Xichuan Zhou, Lingfeng Yan, Rui Ding, Chukwuemeka Clinton Atabansi, Jing Nie, Lihui Chen, Yujie Feng, Haijun Liu

{"title":"MIT-SAM: Medical Image-Text SAM with Mutually Enhanced Heterogeneous Features Fusion for Medical Image Segmentation.","authors":"Xichuan Zhou, Lingfeng Yan, Rui Ding, Chukwuemeka Clinton Atabansi, Jing Nie, Lihui Chen, Yujie Feng, Haijun Liu","doi":"10.1109/JBHI.2025.3561425","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3561425","url":null,"abstract":"In recent times, leveraging lesion text as supplementary data to enhance the performance of medical image segmentation models has garnered attention. Previous approaches only used attention mechanisms to integrate image and text features, while not effectively utilizing the highly condensed textual semantic information in improving the fused features, resulting in inaccurate lesion segmentation. This paper introduces a novel approach, the Medical Image-Text Segment Anything Model (MIT-SAM), for text-assisted medical image segmentation. Specifically, we introduce the SAM-enhanced image encoder and a Bert-based text encoder to extract heterogeneous features. To better leverage the highly condensed textual semantic information for heterogeneous feature fusion, such as crucial details like position and quantity, we propose the image-text interactive fusion (ITIF) block and self-supervised text reconstruction (SSTR) method. The ITIF block facilitates the mutual enhancement of homogeneous information among heterogeneous features and the SSTR method empowers the model to capture crucial details concerning lesion text, including location, quantity, and other key aspects. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on the QaTa-COV19 and MosMedData+ datasets. The code of MIT-SAM is available at https://github.com/jojodan514/MIT-SAM.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedKDC: Consensus-Driven Knowledge Distillation for Personalized Federated Learning in EEG-Based Emotion Recognition. 基于脑电图的情感识别中个性化联邦学习的共识驱动知识蒸馏。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-16 DOI: 10.1109/JBHI.2025.3562090

Xihang Qiu, Wanyong Qiu, Ye Zhang, Kun Qian, Chun Li, Bin Hu, Bjorn W Schuller, Yoshiharu Yamamoto

{"title":"FedKDC: Consensus-Driven Knowledge Distillation for Personalized Federated Learning in EEG-Based Emotion Recognition.","authors":"Xihang Qiu, Wanyong Qiu, Ye Zhang, Kun Qian, Chun Li, Bin Hu, Bjorn W Schuller, Yoshiharu Yamamoto","doi":"10.1109/JBHI.2025.3562090","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3562090","url":null,"abstract":"Federated learning (FL) has gained prominence in electroencephalogram (EEG)-based emotion recognition because of its ability to enable secure collaborative training without centralized data. However, traditional FL faces challenges due to model and data heterogeneity in smart healthcare settings. For example, medical institutions have varying computational resources, which creates a need for personalized local models. Moreover, EEG data from medical institutions typically face data heterogeneity issues stemming from limitations in participant availability, ethical constraints, and cultural differences among subjects, which can slow model convergence and degrade model performance. To address these challenges, we propose FedKDC, a novel FL framework that incorporates clustered knowledge distillation (CKD). This method introduces a consensus-based distributed learning mechanism to facilitate the clustering process. It then enhances the convergence speed through intraclass distillation and reduces the negative impact of heterogeneity through interclass distillation. Additionally, we introduce a DriftGuard mechanism to mitigate client drift, along with an entropy reducer to decrease the entropy of aggregated knowledge. The framework is validated on the SEED, SEED-IV, SEED-FRA, and SEED-GER datasets, demonstrating its effectiveness in scenarios where both the data and the models are heterogeneous. Experimental results show that FedKDC outperforms other FL frameworks in emotion recognition, achieving a maximum average accuracy of $85.2%$, and in convergence efficiency, with faster and more stable convergence. Our code is made publicly available at: https://github.com/wdqdp/FedKDC.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-modal Disease Prediction with Hierarchical Self-supervised Learning. 基于分层自监督学习的多模态疾病预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-16 DOI: 10.1109/JBHI.2025.3561546

Zhe Qu, Taihua Chen, Xin Zhou, Fanglin Zhu, Wei Guo, Yonghui Xu, Yixin Zhang, Lizhen Cui

{"title":"Multi-modal Disease Prediction with Hierarchical Self-supervised Learning.","authors":"Zhe Qu, Taihua Chen, Xin Zhou, Fanglin Zhu, Wei Guo, Yonghui Xu, Yixin Zhang, Lizhen Cui","doi":"10.1109/JBHI.2025.3561546","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3561546","url":null,"abstract":"The proliferation of healthcare data sources, including diverse imaging modalities and biochemical measurements, has created unprecedented opportunities for comprehensive disease prediction. Multi-modal clinical data, encompassing medical imaging reports, biochemical assays, and longitudinal clinical records, provides a rich foundation for developing sophisticated diagnostic models. Graph Neural Networks (GNNs) have emerged as a leading methodological framework, distinguished by their capacity to model complex inter-patient relationships and capture community structures within patient data. Despite their promise, current GNN-based approaches exhibit limitations in handling noisy, low-quality data and often impose overly restrictive graph smoothness constraints. These limitations can obscure patient-specific variations and compromise model robustness. To overcome these challenges, we propose HierSSL (Hierarchical Self-Supervised Learning), a novel multi-modal disease prediction framework that enhances representational learning through dual-scale self-supervision mechanisms operating at both local and global levels. HierSSL's architecture specifically addresses two critical aspects: 1) the capture of local inter-modality dependencies and global community patterns, and 2) the optimization of multi-modal feature integration through an innovative combination of feature consistency constraints and graph contrastive learning. Empirical evaluation across two distinct disease prediction datasets demonstrates that HierSSL achieves statistically significant performance improvements compared to state-of-the-art methods, highlighting its efficacy in robust multi-modal data integration for disease prediction tasks.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate and Real-time Hierarchical Ensemble Network for Activity Classification in Construction Worker. 建筑工人活动分类的精确实时分层集成网络。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-04-16 DOI: 10.1109/JBHI.2025.3561380

Guoyu Zuo, Qifei Wu, Wenbin Gao, Cheng Li, Liangkun Sun, Shuangyue Yu

{"title":"Accurate and Real-time Hierarchical Ensemble Network for Activity Classification in Construction Worker.","authors":"Guoyu Zuo, Qifei Wu, Wenbin Gao, Cheng Li, Liangkun Sun, Shuangyue Yu","doi":"10.1109/JBHI.2025.3561380","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3561380","url":null,"abstract":"Accurate and real-time locomotion classification is crucial for exoskeletons to assist construction workers in completing multiple tasks. However, state-of-the-art algorithms for classifying multiple activities face multifaceted challenges in both accuracy and real-time capability. In addition, advanced studies typically provide a single solution based on certain sensor combinations, which may have an indirect impact on different assistive devices (e.g., an algorithm using feet IMUs is not suited for bilateral portable hip exoskeletons or unilateral knee exoskeletons), limiting its practicality and applicability in diverse applications. To fill these two gaps, first, we developed a novel hierarchical ensemble network framework that can accurately and real-time classify 11 typical lower limb activities of construction workers. Second, building upon this hierarchical ensemble network framework, we developed 6 configurations wearing IMU sensors on different body segments, which are potentially used for different wearable devices. Experimental results with leave-one-out cross-validation obtained from 10 able-bodied subjects validated the effectiveness of the proposed algorithm. Compared to the baseline ANN-based algorithm, our algorithm under 6 configurations on average was able to improve accuracy, precision, recall, and F1-score by 4.97%, 3.40%, 4.97%, and 5.31%, respectively, and reduce the number of parameters and inference time by 71.86% and 47.85%, respectively. This study showcases multiple solutions with different wearable sensor configurations, offering high accuracy and strong real-time performance for classifying multiple activities, which can be deployed to controllers for multiple types of assistive devices targeting construction workers.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144008761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0