Medical & Biological Engineering & Computing最新文献_第2页

Segmentation of the left atrial appendage based on fusion attention. 基于融合注意力的左心房阑尾分割。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-10 DOI: 10.1007/s11517-024-03104-0

Guodong Zhang, Kaichao Liang, Yanlin Li, Tingyu Liang, Zhaoxuan Gong, Ronghui Ju, Dazhe Zhao, Zhuoning Zhang

{"title":"Segmentation of the left atrial appendage based on fusion attention.","authors":"Guodong Zhang, Kaichao Liang, Yanlin Li, Tingyu Liang, Zhaoxuan Gong, Ronghui Ju, Dazhe Zhao, Zhuoning Zhang","doi":"10.1007/s11517-024-03104-0","DOIUrl":"10.1007/s11517-024-03104-0","url":null,"abstract":"In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging. In this paper, we propose a lightweight attention mechanism called fusion attention, which imitates human visual behavior. We process the 3D image of the LAA using a method that involves overview observation followed by detailed observation. In the overview observation stage, the image features are pooled along the three dimensions of length, width, and height. The obtained features from the three dimensions are then separately input into the spatial attention and channel attention modules to learn the regions of interest. In the detailed observation stage, the attention results from the previous stage are fused using element-wise multiplication and combined with the original feature map to enhance feature learning. The fusion attention mechanism was evaluated on a left atrial appendage dataset provided by Liaoning Provincial People's Hospital, resulting in an average Dice coefficient of 0.8855. The results indicate that the fusion attention mechanism achieves better segmentation results on 3D images compared to existing lightweight attention mechanisms.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899456","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 quasi-realistic computational model development and flow field study of the human upper and central airways. 人体上气道和中央气道的准真实计算模型开发和流场研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-17 DOI: 10.1007/s11517-024-03117-9

Mohammad Reza Rezazadeh, Alireza Dastan, Sasan Sadrizadeh, Omid Abouali

{"title":"A quasi-realistic computational model development and flow field study of the human upper and central airways.","authors":"Mohammad Reza Rezazadeh, Alireza Dastan, Sasan Sadrizadeh, Omid Abouali","doi":"10.1007/s11517-024-03117-9","DOIUrl":"10.1007/s11517-024-03117-9","url":null,"abstract":"The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka's deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka's model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15 l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140959116","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

The ECG abnormalities in persons with chronic disorders of consciousness. 慢性意识障碍患者的心电图异常。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-16 DOI: 10.1007/s11517-024-03129-5

Xiaodan Tan, Minmin Luo, Qiuyi Xiao, Xiaochun Zheng, Jiajia Kang, Daogang Zha, Qiuyou Xie, Chang'an A Zhan

{"title":"The ECG abnormalities in persons with chronic disorders of consciousness.","authors":"Xiaodan Tan, Minmin Luo, Qiuyi Xiao, Xiaochun Zheng, Jiajia Kang, Daogang Zha, Qiuyou Xie, Chang'an A Zhan","doi":"10.1007/s11517-024-03129-5","DOIUrl":"10.1007/s11517-024-03129-5","url":null,"abstract":"We aimed to investigate the electrocardiogram (ECG) features in persons with chronic disorders of consciousness (DOC, ≥ 29 days since injury, DSI) resulted from the most severe brain damages. The ECG data from 30 patients with chronic DOC and 18 healthy controls (HCs) were recorded during resting wakefulness state for about five minutes. The patients were classified into vegetative state (VS) and minimally conscious state (MCS). Eight ECG metrics were extracted for comparisons between the subject subgroups, and regression analysis of the metrics were conducted on the DSI (29-593 days). The DOC patients exhibit a significantly higher heart rate (HR, p = 0.009) and lower values for SDNN (p = 0.001), CVRR (p = 0.009), and T-wave amplitude (p < 0.001) compared to the HCs. However, there're no significant differences in QRS, QT, QTc, or ST amplitude between the two groups (p > 0.05). Three ECG metrics of the DOC patients-HR, SDNN, and CVRR-are significantly correlated with the DSI. The ECG abnormalities persist in chronic DOC patients. The abnormalities are mainly manifested in the rhythm features HR, SDNN and CVRR, but not the waveform features such as QRS width, QT, QTc, ST and T-wave amplitudes.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946346","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

Mixture-of-experts and semantic-guided network for brain tumor segmentation with missing MRI modalities. 针对缺失磁共振成像模式的脑肿瘤分割的专家混合和语义引导网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-25 DOI: 10.1007/s11517-024-03130-y

Siyu Liu, Haoran Wang, Shiman Li, Chenxi Zhang

{"title":"Mixture-of-experts and semantic-guided network for brain tumor segmentation with missing MRI modalities.","authors":"Siyu Liu, Haoran Wang, Shiman Li, Chenxi Zhang","doi":"10.1007/s11517-024-03130-y","DOIUrl":"10.1007/s11517-024-03130-y","url":null,"abstract":"Accurate brain tumor segmentation with multi-modal MRI images is crucial, but missing modalities in clinical practice often reduce accuracy. The aim of this study is to propose a mixture-of-experts and semantic-guided network to tackle the issue of missing modalities in brain tumor segmentation. We introduce a transformer-based encoder with novel mixture-of-experts blocks. In each block, four modality experts aim for modality-specific feature learning. Learnable modality embeddings are employed to alleviate the negative effect of missing modalities. We also introduce a decoder guided by semantic information, designed to pay higher attention to various tumor regions. Finally, we conduct extensive comparison experiments with other models as well as ablation experiments to validate the performance of the proposed model on the BraTS2018 dataset. The proposed model can accurately segment brain tumor sub-regions even with missing modalities. It achieves an average Dice score of 0.81 for the whole tumor, 0.66 for the tumor core, and 0.52 for the enhanced tumor across the 15 modality combinations, achieving top or near-top results in most cases, while also exhibiting a lower computational cost. Our mixture-of-experts and sematic-guided network achieves accurate and reliable brain tumor segmentation results with missing modalities, indicating its significant potential for clinical applications. Our source code is already available at https://github.com/MaggieLSY/MESG-Net .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141092267","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

The origin of intraluminal pressure waves in gastrointestinal tract. 胃肠道腔内压力波的起源。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-24 DOI: 10.1007/s11517-024-03128-6

Swati Sharma, Martin L Buist

{"title":"The origin of intraluminal pressure waves in gastrointestinal tract.","authors":"Swati Sharma, Martin L Buist","doi":"10.1007/s11517-024-03128-6","DOIUrl":"10.1007/s11517-024-03128-6","url":null,"abstract":"The gastrointestinal (GI) peristalsis is an involuntary wave-like contraction of the GI wall that helps to propagate food along the tract. Many GI diseases, e.g., gastroparesis, are known to cause motility disorders in which the physiological contractile patterns of the wall get disrupted. Therefore, to understand the pathophysiology of these diseases, it is necessary to understand the mechanism of GI motility. We present a coupled electromechanical model to describe the mechanism of GI motility and the transduction pathway of cellular electrical activities into mechanical deformation and the generation of intraluminal pressure (IP) waves in the GI tract. The proposed model consolidates a smooth muscle cell (SMC) model, an actin-myosin interaction model, a hyperelastic constitutive model, and a Windkessel model to construct a coupled model that can describe the origin of peristaltic contractions in the intestine. The key input to the model is external electrical stimuli, which are converted into mechanical contractile waves in the wall. The model recreated experimental observations efficiently and was able to establish a relationship between change in luminal volume and pressure with the compliance of the GI wall and the peripheral resistance to bolus flow. The proposed model will help us understand the GI tract's function in physiological and pathophysiological conditions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141087748","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

BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification. BranchLabelNet：使用分割和分组多标签分类的人体气道解剖标签法

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-23 DOI: 10.1007/s11517-024-03119-7

Ngan-Khanh Chau, Truong-Thanh Ma, Woo Jin Kim, Chang Hyun Lee, Gong Yong Jin, Kum Ju Chae, Sanghun Choi

{"title":"BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification.","authors":"Ngan-Khanh Chau, Truong-Thanh Ma, Woo Jin Kim, Chang Hyun Lee, Gong Yong Jin, Kum Ju Chae, Sanghun Choi","doi":"10.1007/s11517-024-03119-7","DOIUrl":"10.1007/s11517-024-03119-7","url":null,"abstract":"Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081426","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

Enhancing clinical diagnostics: novel denoising methodology for brain MRI with adaptive masking and modified non-local block. 增强临床诊断：利用自适应遮蔽和修改的非局部块对脑部磁共振成像进行去噪的新方法。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-18 DOI: 10.1007/s11517-024-03122-y

A Velayudham, K Madhan Kumar, Krishna Priya M S

{"title":"Enhancing clinical diagnostics: novel denoising methodology for brain MRI with adaptive masking and modified non-local block.","authors":"A Velayudham, K Madhan Kumar, Krishna Priya M S","doi":"10.1007/s11517-024-03122-y","DOIUrl":"10.1007/s11517-024-03122-y","url":null,"abstract":"Medical image denoising has been a subject of extensive research, with various techniques employed to enhance image quality and facilitate more accurate diagnostics. The evolution of denoising methods has highlighted impressive results but struggled to strike equilibrium between noise reduction and edge preservation which limits its applicability in various domains. This paper manifests the novel methodology that integrates an adaptive masking strategy, transformer-based U-Net Prior generator, edge enhancement module, and modified non-local block (MNLB) for denoising brain MRI clinical images. The adaptive masking strategy maintains the vital information through dynamic mask generation while the prior generator by capturing hierarchical features regenerates the high-quality prior MRI images. Finally, these images are fed to the edge enhancement module to boost structural information by maintaining crucial edge details, and the MNLB produces the denoised output by deriving non-local contextual information. The comprehensive experimental assessment is performed by employing two datasets namely the brain tumor MRI dataset and Alzheimer's dataset for diverse metrics and compared with conventional denoising approaches. The proposed denoising methodology achieves a PSNR of 40.965 and SSIM of 0.938 on the Alzheimer's dataset and also achieves a PSNR of 40.002 and SSIM of 0.926 on the brain tumor MRI dataset at a noise level of 50% revealing its supremacy in noise minimization. Furthermore, the impact of different masking ratios on denoising performance is analyzed which reveals that the proposed method showed PSNR of 40.965, SSIM of 0.938, MAE of 5.847, and MSE of 3.672 at the masking ratio of 60%. Moreover, the findings pave the way for the advancement of clinical image processing, facilitating precise detection of tumors in clinical MRI images.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140959489","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

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors. DDLA：基于连续葡萄糖传感器的糖尿病视网膜病变诊断双深潜自动编码器。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-22 DOI: 10.1007/s11517-024-03120-0

Rui Tao, Hongru Li, Jingyi Lu, Youhe Huang, Yaxin Wang, Wei Lu, Xiaopeng Shao, Jian Zhou, Xia Yu

{"title":"DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.","authors":"Rui Tao, Hongru Li, Jingyi Lu, Youhe Huang, Yaxin Wang, Wei Lu, Xiaopeng Shao, Jian Zhou, Xia Yu","doi":"10.1007/s11517-024-03120-0","DOIUrl":"10.1007/s11517-024-03120-0","url":null,"abstract":"The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076646","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 clinical decision support system for diagnosis and severity quantification of lumbosacral radiculopathy using intramuscular electromyography signals.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-09-19 DOI: 10.1007/s11517-024-03196-8

Farshid Hamtaei Pour Shirazi, Hossein Parsaei, Alireza Ashraf

{"title":"A clinical decision support system for diagnosis and severity quantification of lumbosacral radiculopathy using intramuscular electromyography signals.","authors":"Farshid Hamtaei Pour Shirazi, Hossein Parsaei, Alireza Ashraf","doi":"10.1007/s11517-024-03196-8","DOIUrl":"https://doi.org/10.1007/s11517-024-03196-8","url":null,"abstract":"Interpreting intramuscular electromyography (iEMG) signals for diagnosing and quantifying the severity of lumbosacral radiculopathy is challenging due to the subjective evaluation of signals. To address this limitation, a clinical decision support system (CDSS) was developed for the diagnosis and quantification of the severity of lumbosacral radiculopathy based on intramuscular electromyography (iEMG) signals. The CDSS uses the EMG interference pattern method (QEMG IP) to directly extract features from the iEMG signal and provide a quantitative expression of injury severity for each muscle and overall radiculopathy severity. From 126 time and frequency domain features, a set of five features, including the crest factor, mean absolute value, peak frequency, zero crossing count, and intensity, were selected. These features were derived from raw iEMG signals, empirical mode decomposition, and discrete wavelet transform, and the wrapper method was utilized to determine the most significant features. The CDSS was trained and tested on a dataset of 75 patients, achieving an accuracy of 93.3%, sensitivity of 93.3%, and specificity of 96.6%. The system shows promise in assisting physicians in diagnosing lumbosacral radiculopathy with high accuracy and consistency using iEMG data. The CDSS's objective and standardized diagnostic process, along with its potential to reduce the time and effort required by physicians to interpret EMG signals, makes it a potentially valuable tool for clinicians in the diagnosis and management of lumbosacral radiculopathy. Future work should focus on validating the system's performance in diverse clinical settings and patient populations.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299625","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

Operational space robust impedance control of the redundant surgical robot for minimally invasive surgery 用于微创手术的冗余手术机器人的操作空间稳健阻抗控制

IF 3.2 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-09-19 DOI: 10.1007/s11517-024-03198-6

Fang Huang, Hongqiang Sang, Fen Liu, Rui Han

{"title":"Operational space robust impedance control of the redundant surgical robot for minimally invasive surgery","authors":"Fang Huang, Hongqiang Sang, Fen Liu, Rui Han","doi":"10.1007/s11517-024-03198-6","DOIUrl":"https://doi.org/10.1007/s11517-024-03198-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3>The motion accuracy, compliance, and control smoothness for the surgical robot are of great importance to improve the safety of human-robot interaction. However, the end effector that interacts with soft tissue during surgery affects the dynamics of the robot. The control performance of the controller may be decreased if the changing dynamics are not identified and updated in time. This paper proposes a robust impedance controller for the redundant remote center of motion manipulator influenced by external disturbances, including external torque, uncertainties, and unmodeled terms in the dynamics. To achieve the desired impedance, a continuously switching sliding manifold is proposed. When the sliding manifold is driven to zero, the motion error will converge to a bounded region. This can overcome the adverse effects of external disturbances while guaranteeing motion accuracy and compliance. Chattering of the sliding mode control is alleviated through the formulated continuously switching sliding manifold and integrated nonlinear disturbance observer. Simulations and experiments demonstrate that the proposed controller has excellent motion accuracy, compliance, and control smoothness. This provides potential application prospects for the redundant surgical robot to guarantee safe human-robot interaction.<h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266969","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