Medical & Biological Engineering & Computing最新文献_第10页

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification. SleepBoost：基于多级树的集合模型，用于自动睡眠阶段分类。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-09-01 Epub Date: 2024-05-03 DOI: 10.1007/s11517-024-03096-x

Akib Zaman, Shiu Kumar, Swakkhar Shatabda, Iman Dehzangi, Alok Sharma

{"title":"SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.","authors":"Akib Zaman, Shiu Kumar, Swakkhar Shatabda, Iman Dehzangi, Alok Sharma","doi":"10.1007/s11517-024-03096-x","DOIUrl":"10.1007/s11517-024-03096-x","url":null,"abstract":"Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140860919","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

Finite element analysis of a new preoperative traction for cervical kyphosis: suspensory traction. 颈椎后凸术前牵引新方法的有限元分析：悬吊牵引。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-09-01 Epub Date: 2024-05-06 DOI: 10.1007/s11517-024-03113-z

Hongyu Chen, Tianchi Wu, Shengfa Pan, Li Zhang, Yanbin Zhao, Xin Chen, Yu Sun, William W Lu, Feifei Zhou

{"title":"Finite element analysis of a new preoperative traction for cervical kyphosis: suspensory traction.","authors":"Hongyu Chen, Tianchi Wu, Shengfa Pan, Li Zhang, Yanbin Zhao, Xin Chen, Yu Sun, William W Lu, Feifei Zhou","doi":"10.1007/s11517-024-03113-z","DOIUrl":"10.1007/s11517-024-03113-z","url":null,"abstract":"A finite element model of cervical kyphosis was established to analyze the stress of cervical spine under suspensory traction and to explore the mechanism and effect of it. A patient with typical cervical kyphosis (C2-C5) underwent CT scan imaging, and 3D slicer was used to reconstruct the C2 to T2 vertebral bodies. The reconstructed data was imported into Hypermesh 2020 and Abaqus 2017 for meshing and finite element analysis. The changes of the kyphotic angle and the von Mises stress on the annulus fibrosus of each intervertebral disc and ligaments were analyzed under suspensory traction conditions. With the increase of suspensory traction weight, the overall kyphosis of cervical spine showed a decreasing trend. The correction of kyphosis was mainly contributed by the change of kyphotic segments. The kyphotic angle of C2-C5 was corrected from 45° to 13° finally. In cervical intervertebral discs, the stress was concentrated to anterior and posterior part, except for C4-5. The stress of the anterior longitudinal ligament (ALL) decreased from the rostral to the caudal, and the high level von Mises stress of the kyphotic segments appeared at C2-C3, C3-C4, and C4-C5. The roles of the other ligaments were not obvious. The kyphotic angle was significantly reduced by the suspensory traction. Shear effect due to the high von Mises stress in the anterior and posterior parts of annulus fibrosus and the tension on the anterior longitudinal ligament play a role in the correction of cervical kyphosis.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140855758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data augmentation for generating synthetic electrogastrogram time series. 用于生成合成胃电图时间序列的数据增强技术。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-09-01 Epub Date: 2024-05-06 DOI: 10.1007/s11517-024-03112-0

Nadica Miljković, Nikola Milenić, Nenad B Popović, Jaka Sodnik

{"title":"Data augmentation for generating synthetic electrogastrogram time series.","authors":"Nadica Miljković, Nikola Milenić, Nenad B Popović, Jaka Sodnik","doi":"10.1007/s11517-024-03112-0","DOIUrl":"10.1007/s11517-024-03112-0","url":null,"abstract":"To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition. DEC-DRR：用于识别糖尿病视网膜病变的深度集合分类模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-09-01 Epub Date: 2024-05-07 DOI: 10.1007/s11517-024-03076-1

L B Lisha, C Helen Sulochana

{"title":"DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition.","authors":"L B Lisha, C Helen Sulochana","doi":"10.1007/s11517-024-03076-1","DOIUrl":"10.1007/s11517-024-03076-1","url":null,"abstract":"Most diabetes patients are liable to have diabetic retinopathy (DR); however, the majority of them might not be even aware of the ailment. Therefore, early detection and treatment of DR are necessary to prevent vision loss. But, avoiding DR is not a simple process. An ophthalmologist can typically identify DR through an optical evaluation of the fundus and through the evaluation of color pictures. However, due to the increased count of DR patients, this could not be possible as it consumes more time. To rectify this problem, a novel deep ensemble-based DR classification technique is developed in this work. Initially, a Wiener filter (WF) is applied for preprocessing the image. Then, the enhanced U-Net-based segmentation process is done. Subsequent to the segmentation process, features are extracted that include statistical features, inferior superior nasal temporal (ISNT), cup to disc ratio (CDR), and improved LGBP as well. Further, deep ensemble classifiers (DEC) like CNN, Bi-GRU, and DMN are used to recognize the disease. The outcomes from DMN, CNN, and Bi-GRU are then subjected to improved SLF. Additionally, the weights of DMN, CNN, and Bi-GRU are adjusted via pelican updated Tasmanian devil optimization (PU-TDO). Finally, outputs on DR (microaneurysms, hemorrhages, hard exudates, and soft exudates) are obtained. The performance of DEC + PU-TDO for diabetic retinopathy is computed over extant models with regard to different measures for four datasets. The results on accuracy using the DEC + PU-TDO scheme for the IDRID dataset is maximum around 0.975 at 90th LP while other models have less accuracy. The FPR of DEC + PU-TDO is less around 0.039 at the 90th LP for the SUSTech-SYSU dataset, while other extant models have maximum FPR.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859850","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

Recognition of diabetic retinopathy and macular edema using deep learning. 利用深度学习识别糖尿病视网膜病变和黄斑水肿。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-09-01 Epub Date: 2024-04-30 DOI: 10.1007/s11517-024-03105-z

Fathe Jeribi, Tahira Nazir, Marriam Nawaz, Ali Javed, Mohammed Alhameed, Ali Tahir

{"title":"Recognition of diabetic retinopathy and macular edema using deep learning.","authors":"Fathe Jeribi, Tahira Nazir, Marriam Nawaz, Ali Javed, Mohammed Alhameed, Ali Tahir","doi":"10.1007/s11517-024-03105-z","DOIUrl":"10.1007/s11517-024-03105-z","url":null,"abstract":"Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associated with diabetes and if left untreated, and they can lead to permanent blindness. Traditional methods for screening these conditions rely on manual image analysis by experts, which can be time-consuming and costly due to the scarcity of such experts. To overcome the aforementioned challenges, we present the Modified CornerNet approach with DenseNet-100. This system aims to localize and classify lesions associated with DR and DME. To train our model, we first generate annotations for input samples. These annotations likely include information about the location and type of lesions within the retinal images. DenseNet-100 is a deep CNN used for feature extraction, and CornerNet is a one-stage object detection model. CornerNet is known for its ability to accurately localize small objects, which makes it suitable for detecting lesions in retinal images. We assessed our technique on two challenging datasets, EyePACS and IDRiD. These datasets contain a diverse range of retinal images, which is important to estimate the performance of our model. Further, the proposed model is also tested in the cross-corpus scenario on two challenging datasets named APTOS-2019 and Diaretdb1 to assess the generalizability of our system. According to the accomplished analysis, our method outperformed the latest approaches in terms of both qualitative and quantitative results. The ability to effectively localize small abnormalities and handle over-fitted challenges is highlighted as a key strength of the suggested framework which can assist the practitioners in the timely recognition of such eye ailments.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140867045","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

Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images? 将图像分辨率纳入神经网络能否提高超声图像中肾肿瘤的分类性能？

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-08-31 DOI: 10.1007/s11517-024-03188-8

Haihao He, Yuhan Liu, Xin Zhou, Jia Zhan, Changyan Wang, Yiwen Shen, Haobo Chen, Lin Chen, Qi Zhang

{"title":"Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?","authors":"Haihao He, Yuhan Liu, Xin Zhou, Jia Zhan, Changyan Wang, Yiwen Shen, Haobo Chen, Lin Chen, Qi Zhang","doi":"10.1007/s11517-024-03188-8","DOIUrl":"https://doi.org/10.1007/s11517-024-03188-8","url":null,"abstract":"Deep learning has been widely used in ultrasound image analysis, and it also benefits kidney ultrasound interpretation and diagnosis. However, the importance of ultrasound image resolution often goes overlooked within deep learning methodologies. In this study, we integrate the ultrasound image resolution into a convolutional neural network and explore the effect of the resolution on diagnosis of kidney tumors. In the process of integrating the image resolution information, we propose two different approaches to narrow the semantic gap between the features extracted by the neural network and the resolution features. In the first approach, the resolution is directly concatenated with the features extracted by the neural network. In the second approach, the features extracted by the neural network are first dimensionally reduced and then combined with the resolution features to form new composite features. We compare these two approaches incorporating the resolution with the method without incorporating the resolution on a kidney tumor dataset of 926 images consisting of 211 images of benign kidney tumors and 715 images of malignant kidney tumors. The area under the receiver operating characteristic curve (AUC) of the method without incorporating the resolution is 0.8665, and the AUCs of the two approaches incorporating the resolution are 0.8926 (P < 0.0001) and 0.9135 (P < 0.0001) respectively. This study has established end-to-end kidney tumor classification systems and has demonstrated the benefits of integrating image resolution, showing that incorporating image resolution into neural networks can more accurately distinguish between malignant and benign kidney tumors in ultrasound images.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114134","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

Evaluation of instability in patients with chronic vestibular syndrome using dynamic stability indicators. 利用动态稳定性指标评估慢性前庭综合征患者的不稳定性。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-08-30 DOI: 10.1007/s11517-024-03185-x

Yingnan Ma, Xing Gao, Li Wang, Ziyang Lyu, Fei Shen, Haijun Niu

{"title":"Evaluation of instability in patients with chronic vestibular syndrome using dynamic stability indicators.","authors":"Yingnan Ma, Xing Gao, Li Wang, Ziyang Lyu, Fei Shen, Haijun Niu","doi":"10.1007/s11517-024-03185-x","DOIUrl":"https://doi.org/10.1007/s11517-024-03185-x","url":null,"abstract":"Gait abnormalities are common in patients with chronic vestibular syndrome (CVS), and stability analysis and gait feature recognition in CVS patients have clinical significance for diagnosing CVS. This study explored two-dimensional dynamic stability indicators for evaluating gait instability in patients with CVS. The Center of Mass acceleration (COMa) peak of CVS patients was significantly faster than that of the control group (p < 0.05), closer to the back of the body, and slower at the Toe-off (TO) moment, which enlarged the Center of Mass position-velocity combination proportion within the Region of Velocity Stability (ROSv). The sensitivity, specificity, and accuracy of the Center of Mass velocity (COMv) or COMa peaks were 75.0%, 93.7%, and 90.2% for CVS patients and control groups, respectively. The two-dimensional ROSv parameters improved sensitivity, specificity, and accuracy in judging gait instability in patients over traditional dynamic stability parameters. Dynamic stability parameters quantitatively described the differences in dynamic stability during walking between patients with different degrees of CVS and those in the control group. As CVS impairment increases, the patient's dynamic stability decreases. This study provides a reference for the quantitative evaluation of gait stability in patients with CVS.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114136","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

Role of intra-lamellar collagen and hyaluronan nanostructures in annulus fibrosus on lumbar spine biomechanics: insights from molecular mechanics-finite element-based multiscale analyses. 纤维环内胶原蛋白和透明质酸纳米结构对腰椎生物力学的作用：基于分子力学-有限元多尺度分析的启示。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-08-26 DOI: 10.1007/s11517-024-03184-y

Shambo Bhattacharya, Devendra K Dubey

{"title":"Role of intra-lamellar collagen and hyaluronan nanostructures in annulus fibrosus on lumbar spine biomechanics: insights from molecular mechanics-finite element-based multiscale analyses.","authors":"Shambo Bhattacharya, Devendra K Dubey","doi":"10.1007/s11517-024-03184-y","DOIUrl":"https://doi.org/10.1007/s11517-024-03184-y","url":null,"abstract":"Annulus fibrosus' (AF) ability to transmit multi-directional spinal motion is contributed by a combination of chemical interactions among biomolecular constituents-collagen type I (COL-I), collagen type II (COL-II), and proteoglycans (aggrecan and hyaluronan)-and mechanical interactions at multiple length scales. However, the mechanistic role of such interactions on spinal motion is unclear. The present work employs a molecular mechanics-finite element (FE) multiscale approach to investigate the mechanistic role of molecular-scale collagen and hyaluronan nanostructures in AF, on spinal motion. For this, an FE model of the lumbar segment is developed wherein a multiscale model of AF collagen fiber, developed from COL-I, COL-II, and hyaluronan using the molecular dynamics-cohesive finite element multiscale method, is incorporated. Analyses show AF collagen fibers primarily contribute to axial rotation (AR) motion, owing to angle-ply orientation. Maximum fiber strain values of 2.45% in AR, observed at the outer annulus, are 25% lower than the reported values. This indicates native collagen fibers are softer, attributed to the softer non-fibrillar matrix and higher interfibrillar sliding. Additionally, elastic zone stiffness of 8.61 Nm/° is observed to be 20% higher than the reported range, suggesting native AF lamellae exhibit lower stiffness, resulting from inter-collagen fiber bundle sliding. The presented study has further implications towards the hierarchy-driven designing of AF-substitute materials.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057091","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

mm3DSNet: multi-scale and multi-feedforward self-attention 3D segmentation network for CT scans of hepatobiliary ducts. mm3DSNet：用于肝胆管 CT 扫描的多尺度、多前馈自注意力三维分割网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-08-23 DOI: 10.1007/s11517-024-03183-z

Yinghong Zhou, Yiying Xie, Nian Cai, Yuchen Liang, Ruifeng Gong, Ping Wang

{"title":"mm3DSNet: multi-scale and multi-feedforward self-attention 3D segmentation network for CT scans of hepatobiliary ducts.","authors":"Yinghong Zhou, Yiying Xie, Nian Cai, Yuchen Liang, Ruifeng Gong, Ping Wang","doi":"10.1007/s11517-024-03183-z","DOIUrl":"https://doi.org/10.1007/s11517-024-03183-z","url":null,"abstract":"Image segmentation is a key step of the 3D reconstruction of the hepatobiliary duct tree, which is significant for preoperative planning. In this paper, a novel 3D U-Net variant is designed for CT image segmentation of hepatobiliary ducts from the abdominal CT scans, which is composed of a 3D encoder-decoder and a 3D multi-feedforward self-attention module (MFSAM). To well sufficient semantic and spatial features with high inference speed, the 3D ConvNeXt block is designed as the 3D extension of the 2D ConvNeXt. To improve the ability of semantic feature extraction, the MFSAM is designed to transfer the semantic and spatial features at different scales from the encoder to the decoder. Also, to balance the losses for the voxels and the edges of the hepatobiliary ducts, a boundary-aware overlap cross-entropy loss is proposed by combining the cross-entropy loss, the Dice loss, and the boundary loss. Experimental results indicate that the proposed method is superior to some existing deep networks as well as the radiologist without rich experience in terms of CT segmentation of hepatobiliary ducts, with a segmentation performance of 76.54% Dice and 6.56 HD.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037540","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

Gait pattern modification based on ground contact adaptation using the robot-assisted training platform (RATP). 利用机器人辅助训练平台（RATP）根据地面接触适应性修改步态。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-08-17 DOI: 10.1007/s11517-024-03176-y

Shamanth Shanmuga Prasad, Ulfah Khairiyah Luthfiyani, Youngwoo Kim

{"title":"Gait pattern modification based on ground contact adaptation using the robot-assisted training platform (RATP).","authors":"Shamanth Shanmuga Prasad, Ulfah Khairiyah Luthfiyani, Youngwoo Kim","doi":"10.1007/s11517-024-03176-y","DOIUrl":"10.1007/s11517-024-03176-y","url":null,"abstract":"Robot-assisted rehabilitation and training systems are utilized to improve the functional recovery of individuals with mobility limitations. These systems offer structured rehabilitation through precise human-robot interaction, outperforming traditional physical therapy by delivering advantages such as targeted muscle recovery, optimization of walking patterns, and automated training routines tailored to the user's objectives and musculoskeletal attributes. In our research, we propose the development of a walking simulator that considers user-specific musculoskeletal information to replicate natural walking dynamics, accounting for factors like joint angles, muscular forces, internal user-specific constraints, and external environmental factors. The integration of these factors into robot-assisted training can provide a more realistic rehabilitation environment and serve as a foundation for achieving natural bipedal locomotion. Our research team has developed a robot-assisted training platform (RATP) that generates gait training sets based on user-specific internal and external constraints by incorporating a genetic algorithm (GA). We utilize the Lagrangian multipliers to accommodate requirements from the rehabilitation field to instantly reshape the gait patterns while maintaining their overall characteristics, without an additional gait pattern search process. Depending on the patient's rehabilitation progress, there are times when it is necessary to reorganize the training session by changing training conditions such as terrain conditions, walking speed, and joint range of motion. The proposed method allows gait rehabilitation to be performed while stably satisfying ground contact constraints, even after modifying the training parameters.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141996789","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