Computer Methods in Biomechanics and Biomedical Engineering最新文献_第6页

Type two fuzzy logic control system for powered ankle-foot prosthesis: robust performance against terrain and speed variations. 二类模糊逻辑控制系统用于动力踝足假体：对地形和速度变化的鲁棒性。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-04 DOI: 10.1080/10255842.2025.2506791

Mouaz Al Kouzbary, Joslyn Ker Xin Yeoh, Hamza Al Kouzbary, Jingjing Liu, Hanie Nadia Shasmin, Lai Kuan Tham, Nooranida Arifin, Noor Azuan Abu Osman

{"title":"Type two fuzzy logic control system for powered ankle-foot prosthesis: robust performance against terrain and speed variations.","authors":"Mouaz Al Kouzbary, Joslyn Ker Xin Yeoh, Hamza Al Kouzbary, Jingjing Liu, Hanie Nadia Shasmin, Lai Kuan Tham, Nooranida Arifin, Noor Azuan Abu Osman","doi":"10.1080/10255842.2025.2506791","DOIUrl":"https://doi.org/10.1080/10255842.2025.2506791","url":null,"abstract":"The development of powered prostheses' control systems is drifting away from discontinuous control systems based on the finite state machine (FSM), due to the issues of misclassification. Recent research focused on model-based control systems, most widely used is the hybrid zero dynamic (HZD). However, the HZD system depends on the model accuracy and number of feedback signals. Navigating different terrains is vital for independent mobility for people with lower-limb amputation. In this paper, we propose a control system based on Takagi-Sugeno-Kang (TSK) inference. The fuzzy system is based on Type 2 membership functions to accommodate the gait cycle uncertainties. To fulfil the design procedure, experiments were conducted to capture the ambulation data in different terrains. Twelve individuals participated in four experiments, a set of seven inertial measurement units (IMUs) were placed on the lower body. The data was used to assess the control system behaviour terrains and subjects' data. A model was built to represent the powered ankle-foot prosthesis, where the ground reaction force (GRF) and the target angular position were experimentally extracted and fed to the model. The control system was evaluated using three evaluation parameters (root mean square error (RMSE), mean absolute error (MAE) and normalized cross-correlation). The average RMSE is 3.22 ± 2.7 degree, and a high correlation of 96.73% can be observed. The performance matrix is uniform based on Kruskal-Wallis test (p = 0.9954), and effect size (Cramér's V = 0.02) indicated negligible practical significance of the change of speed and terrain on the control performance.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

HEGNet: EEG and EMG fusion decoding method in motor imagery and actual movement. HEGNet：运动想象与实际运动的脑电与肌电融合解码方法。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-04 DOI: 10.1080/10255842.2025.2512877

Jiuxiang Song, Xiaoke Chai, Xuemin Zhang, Zeping Lv, Feng Wan, Yi Yang, Xinying Shan, Jizhong Liu

引用次数: 0

DeepFusion: early diagnosis of COPD, asthma, and pneumonia using lung sound analysis with a multimodal BiGRU network. DeepFusion：利用多模态BiGRU网络进行肺音分析，早期诊断COPD、哮喘和肺炎。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-04 DOI: 10.1080/10255842.2025.2511228

Prakash Sahu, Santosh Kumar, Ajoy Kumar Behera

引用次数: 0

Enhancing heart disease prediction accuracy with hybrid machine learning. 利用混合机器学习提高心脏病预测的准确性。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-02 DOI: 10.1080/10255842.2025.2510368

Huie Zhang, Caihong Li, Xinzhi Tian, Haijie Shen

引用次数: 0

Numerical investigation of blood rheology in ventricular assist devices: effects on performance and shear stress. 心室辅助装置血液流变学的数值研究：对性能和剪切应力的影响。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-02 DOI: 10.1080/10255842.2025.2512876

Mohamed Bounouib, Mourad Taha-Janan

引用次数: 0

Heart disease classification using hybrid ML schemes and optimization tactics in healthcare. 医疗保健中使用混合ML方案和优化策略的心脏病分类。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-02 DOI: 10.1080/10255842.2025.2510373

Qijia Liu, Fande Kong, Zhengyi Song

{"title":"Heart disease classification using hybrid ML schemes and optimization tactics in healthcare.","authors":"Qijia Liu, Fande Kong, Zhengyi Song","doi":"10.1080/10255842.2025.2510373","DOIUrl":"https://doi.org/10.1080/10255842.2025.2510373","url":null,"abstract":"Problem: Heart disease remains a major contributor to mortality worldwide, and early diagnosis is crucial for treatment. Traditional diagnostic tactics often face challenges regarding accuracy and efficacy. With the rise of ML and decision-making approaches, there is a rising interest in developing automated systems to aid in heart disease detection.Aim: This investigation tries to boost the accuracy of heart disease classification by integrating advanced optimization schemes with machine learning (ML) schemes, specifically the Random Forest Classifier (RFC) and Gaussian Process Classifier, to boost diagnostic performance for heart disease projection.Tactics: Four hybrid schemes were developed by integrating the Golf optimization algorithm (GOA) and Alibaba optimization algorithm with the RF and Gaussian process classifiers. The hybrid schemes were trained and evaluated on a comprehensive database of clinical factors related to heart disease. Data preprocessing included random permutation, missing value imputation, and a 70-30 split into training and test sets.Outcomes: In the recommended schemes, the RF with GOA had the maximum classification accuracy of 95.38%, which is 4.33% higher than the individual RF model. This is greater than the comparative study's best accuracy, which is approximately 92.32%, and demonstrates the efficacy of RFGO in classifying heart disease patients with high accuracy.Conclusion: The RF with GOA significantly improves the accuracy of heart disease classification, illustrating its strong application as a high-performance tool for use in decision support systems in managing cardiovascular health. The results indicate the significance of implementing optimization tactics in ML schemes to boost healthcare diagnostic capabilities.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-20"},"PeriodicalIF":1.7,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

A relationship of tightening torque and initial load of dental implant of nano bio-silica and bamboo fiber-reinforced bio-composite material. 纳米生物二氧化硅和竹纤维增强生物复合材料牙科种植体的拧紧扭矩与初始载荷的关系。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-01 Epub Date: 2024-02-28 DOI: 10.1080/10255842.2024.2320750

Sambhrant Srivastava, Saroj Kumar Sarangi

{"title":"A relationship of tightening torque and initial load of dental implant of nano bio-silica and bamboo fiber-reinforced bio-composite material.","authors":"Sambhrant Srivastava, Saroj Kumar Sarangi","doi":"10.1080/10255842.2024.2320750","DOIUrl":"10.1080/10255842.2024.2320750","url":null,"abstract":"Due to entry of body fluid like saliva, blood, etc. in the dental implant assembly lowers the preload value, thus dental implant abutment tightening torque loses. In this article a novel chitosan-reinforced bamboo and nano bio-silica-reinforced five composite materials (CP, CF, C1, C2, and C3) are fabricated using the hand layup method, and their mechanical, biocompatible, and moisture absorption properties are observed and discussed. The present study examines the impact of friction and Young's modulus on the correlation between torque and starting load in dental implant abutment screws, utilizing the attributes of a bio-composite material. C2 bio-composite composite material exhibits the highest tensile strength (139.442 MPa), flexural strength (183.571 MPa), compressive strength (62.78 MPa), and a minimum value of 1.35% absorption of water. C3 is tested with no cytotoxicity, while C3 and CF exhibit weak biofilm resistance against S. aureus gram-positive bacteria. The C2 bio-composite material demonstrated a maximum initial load of 20 N with a tightening torque of 20 N-cm, under both 0.12 and 0.16 coefficients of friction. The simulated results were compared with several theoretical relations of torque and initial load and found that the Motos equation holds the nearest result to the obtained preload value from finite element analysis. Overall, the experimental findings suggest that the C2 bio-composite material holds significant potential as a prominent material for dental implants or fixtures.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1280-1294"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139991739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of prognostic value of T cell-related genes in cervical cancer. 单细胞和大体积组织转录组的综合免疫学分析揭示了T细胞相关基因在宫颈癌预后中的作用。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-01 Epub Date: 2025-03-10 DOI: 10.1080/10255842.2025.2475483

Yao Fu, Xiubing Zhang, Lili Yu, Guiping Zhang, Xinyu Liu, Wei Ren

{"title":"Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of prognostic value of T cell-related genes in cervical cancer.","authors":"Yao Fu, Xiubing Zhang, Lili Yu, Guiping Zhang, Xinyu Liu, Wei Ren","doi":"10.1080/10255842.2025.2475483","DOIUrl":"10.1080/10255842.2025.2475483","url":null,"abstract":"The relationship between cervical cancer (CESC) and T cells is mainly seen in the anti-tumor functions of T cells. This study aims to identify prognostic genes associated with CESC and T cells, providing a foundation for developing immunotherapy strategies. This study used data from public databases to identify T cell-related prognostic genes for CESC patients through differential expression analysis and single-cell clustering. A prognostic risk model and nomogram were constructed and validated based on these genes. Pseudotime analysis clarified T cell differentiation processes in CESC. Ultimately, Mendelian randomization (MR) was applied to determine the causal relationship between the prognostic genes and CESC. In this study, CXCL2, ANKRD22, SPP1, and C1QB were identified as prognostic genes for CESC. Survival analysis indicated that the survival rate of the high-risk cohort (HRC) was significantly lower compared to that of the low-risk cohort (LRC). A nomogram also demonstrated strong predictive capability. Notably, higher expression levels of prognostic genes were observed during the early stages of T cell differentiation. MR analyses revealed that SPP1 was a risk factor for CESC (OR = 1.165; 95% CI: 1.028-1.320; p = .017), while C1Q8 acted as a protective factor (OR = 0.820; 95% CI: 0.685-0.983; p = .032). CXCL2, ANKRD22, SPP1, and C1QB showed strong prognostic characteristics in CESC and significant predictive capabilities for patient outcomes. The study also emphasized the critical role of T cells in CESC progression.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1333-1353"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli. 混合计算模型描述了人脑非重要脑叶在感知情绪刺激时的贡献。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-01 Epub Date: 2024-02-08 DOI: 10.1080/10255842.2024.2311876

Raghavendra Prasad, Shashikanta Tarai, Arindam Bit

{"title":"Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli.","authors":"Raghavendra Prasad, Shashikanta Tarai, Arindam Bit","doi":"10.1080/10255842.2024.2311876","DOIUrl":"10.1080/10255842.2024.2311876","url":null,"abstract":"Emotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent -deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1127-1153"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139703910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Scalable musculoskeletal model for dynamic simulations of lower body movement. 用于下半身运动动态模拟的可扩展肌肉骨骼模型。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-01 Epub Date: 2024-02-23 DOI: 10.1080/10255842.2024.2316240

Ali Nasr, John McPhee

引用次数: 0