Journal of Medical and Biological Engineering最新文献

英文中文

X-Ray CT-Based Evaluation of Dorsal Rotation Angles and Axes of the 1st–5th Tarsometatarsal Joints 基于x线ct的第1 - 5跗跖关节背侧旋转角度和轴的评价

IF 2 4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-11-24 DOI: 10.1007/s40846-023-00840-w

Satoshi Shimawaki, Koyo Aoki, Hidenori Iwasa, Harushi Mori

Purpose

The tarsometatarsal (TMT) joints play crucial roles in walking and slow running. Some studies have treated the 1st to 5th TMT joints collectively as a single joint. However, few studies have examined each individual joint within the 1st to 5th TMT joints separately. This study sought to determine the rotation angles and axes of the 1st–5th TMT joints as each metatarsal head was dorsally displaced in vivo.

Methods

CT imaging was conducted on 11 subjects under six conditions: a basic posture and isolated metatarsal movement postures in which one of the 1st to 5th metatarsals was dorsally displaced with a load of 55 N. The helical axis parameters (rotation angle and rotation axis) of the metatarsal were calculated by transitioning from the basic posture to each isolated metatarsal movement posture.

Results

Multiple comparison testing on the dorsal rotation angles showed significant differences between all TMT joints, except between the 1st and 4th TMT joints and between the 1st and 5th TMT joints (p < 0.01). The 2nd TMT joint exhibited a significantly lower dorsal rotation angle than the other TMT joints. By transitioning from the 1st to the 5th TMT joints, the transversal plane rotation axis shifted from lateral-anterior to -posterior, while that of the sagittal plane shifted from anterior- to posterior-inferior.

Conclusion

This study provided the rotation angles and axes of the 1st–5th TMT joints in vivo. The data provide a useful reference dataset for selecting diseased feet and developing new foot orthoses.

目的跗跖关节(TMT)在步行和慢跑中起着至关重要的作用。一些研究将第1至第5 TMT关节统称为单个关节。然而，很少有研究单独检查第1至第5个TMT关节内的每个关节。本研究试图确定体内每个跖骨头背侧移位时第1 - 5 TMT关节的旋转角度和轴。方法对11例受试者在基本体位和孤立跖骨运动体位下进行sct成像，其中1 ~ 5跖骨在55 n的载荷下背侧移位，从基本体位过渡到每个孤立跖骨运动体位，计算跖骨的螺旋轴参数(旋转角度和旋转轴)。结果除第1、4关节间、第1、5关节间外，所有关节间背侧旋转角度的多重比较均有统计学差异(p < 0.01)。第二TMT关节的背侧旋转角度明显低于其他关节。通过从第1关节过渡到第5关节，横断面旋转轴由前外侧转向后外侧，矢状面旋转轴由前外侧转向后下方。结论本研究提供了活体TMT第1 ~ 5关节的旋转角度和轴向。这些数据为选择患病足部和开发新型足部矫形器提供了有用的参考数据。

{"title":"X-Ray CT-Based Evaluation of Dorsal Rotation Angles and Axes of the 1st–5th Tarsometatarsal Joints","authors":"Satoshi Shimawaki, Koyo Aoki, Hidenori Iwasa, Harushi Mori","doi":"10.1007/s40846-023-00840-w","DOIUrl":"https://doi.org/10.1007/s40846-023-00840-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>The tarsometatarsal (TMT) joints play crucial roles in walking and slow running. Some studies have treated the 1st to 5th TMT joints collectively as a single joint. However, few studies have examined each individual joint within the 1st to 5th TMT joints separately. This study sought to determine the rotation angles and axes of the 1st–5th TMT joints as each metatarsal head was dorsally displaced in vivo.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>CT imaging was conducted on 11 subjects under six conditions: a basic posture and isolated metatarsal movement postures in which one of the 1st to 5th metatarsals was dorsally displaced with a load of 55 N. The helical axis parameters (rotation angle and rotation axis) of the metatarsal were calculated by transitioning from the basic posture to each isolated metatarsal movement posture.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Multiple comparison testing on the dorsal rotation angles showed significant differences between all TMT joints, except between the 1st and 4th TMT joints and between the 1st and 5th TMT joints (<i>p</i> < 0.01). The 2nd TMT joint exhibited a significantly lower dorsal rotation angle than the other TMT joints. By transitioning from the 1st to the 5th TMT joints, the transversal plane rotation axis shifted from lateral-anterior to -posterior, while that of the sagittal plane shifted from anterior- to posterior-inferior.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This study provided the rotation angles and axes of the 1st–5th TMT joints in vivo. The data provide a useful reference dataset for selecting diseased feet and developing new foot orthoses.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":" 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138492503","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

Transfer Learning Enhanced Cross-Subject Hand Gesture Recognition with sEMG 基于表面肌电信号的迁移学习增强跨主体手势识别

IF 2 4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-11-21 DOI: 10.1007/s40846-023-00837-5

Shenyilang Zhang, Yinfeng Fang, Jiacheng Wan, Guozhang Jiang, Gongfa Li

Purpose

This study explores the emerging field of human physical action classification within human–machine interaction (HMI), with potential applications in assisting individuals with disabilities and robotics. The research focuses on addressing the challenges posed by diverse sEMG signals, aiming for improved cross-subject hand gesture recognition.

Methods

The proposed approach utilizes deep transfer learning technology, employing multi-feature images (MFI) generated through grayscale conversion and RGB mapping of numerical matrices. These MFIs are fed as input into a fine-tuned AlexNet model. Two databases, ISRMyo-I and Ninapro DB1, are employed for experimentation. Rigorous testing is conducted to identify optimal parameters and feature combinations. Data augmentation techniques are applied, doubling the MFI dataset. Cross-subject experiments encompass six wrist gestures from Ninapro DB1 and thirteen gestures from ISRMyo-I.

Results

The study demonstrates substantial performance enhancements. In Ninapro DB1, the mean accuracy achieves 86.16%, showcasing a 13.25% improvement over the best-performing traditional decoding method. Similarly, in ISRMyo-I, a mean accuracy of 70.41% is attained, signifying a 7.4% increase in accuracy compared to traditional methods.

Conclusion

This research establishes a robust framework capable of mitigating cross-user differences in hand gesture recognition based on sEMG signals. By employing deep transfer learning techniques and multi-feature image processing, the study significantly enhances the accuracy of cross-subject hand gesture recognition. This advancement holds promise for enriching human–machine interaction and extending the practical applications of this technology in assisting disabled individuals and robotics.

目的探讨人机交互(HMI)中人体物理动作分类的新兴领域，在帮助残疾人和机器人方面具有潜在的应用前景。研究重点是解决不同表面肌电信号带来的挑战，旨在提高跨主体手势识别。方法利用深度迁移学习技术，利用数值矩阵灰度转换和RGB映射生成的多特征图像(MFI)。这些小额信贷作为输入输入到一个微调的AlexNet模型中。两个数据库，ISRMyo-I和Ninapro DB1，被用于实验。进行了严格的测试，以确定最佳参数和特征组合。应用数据增强技术，使MFI数据集增加一倍。交叉受试者实验包括6种来自Ninapro DB1的手腕手势和13种来自ISRMyo-I的手势。结果该研究显示了显著的性能增强。在Ninapro DB1中，平均准确率达到86.16%，比性能最好的传统解码方法提高了13.25%。同样，在ISRMyo-I中，平均准确率达到70.41%，与传统方法相比准确率提高了7.4%。本研究建立了一个鲁棒的框架，能够减轻基于表面肌电信号的手势识别中的跨用户差异。采用深度迁移学习技术和多特征图像处理，显著提高了跨主体手势识别的准确率。这一进步有望丰富人机交互，并扩展该技术在帮助残疾人和机器人方面的实际应用。

{"title":"Transfer Learning Enhanced Cross-Subject Hand Gesture Recognition with sEMG","authors":"Shenyilang Zhang, Yinfeng Fang, Jiacheng Wan, Guozhang Jiang, Gongfa Li","doi":"10.1007/s40846-023-00837-5","DOIUrl":"https://doi.org/10.1007/s40846-023-00837-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>This study explores the emerging field of human physical action classification within human–machine interaction (HMI), with potential applications in assisting individuals with disabilities and robotics. The research focuses on addressing the challenges posed by diverse sEMG signals, aiming for improved cross-subject hand gesture recognition.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The proposed approach utilizes deep transfer learning technology, employing multi-feature images (MFI) generated through grayscale conversion and RGB mapping of numerical matrices. These MFIs are fed as input into a fine-tuned AlexNet model. Two databases, ISRMyo-I and Ninapro DB1, are employed for experimentation. Rigorous testing is conducted to identify optimal parameters and feature combinations. Data augmentation techniques are applied, doubling the MFI dataset. Cross-subject experiments encompass six wrist gestures from Ninapro DB1 and thirteen gestures from ISRMyo-I.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The study demonstrates substantial performance enhancements. In Ninapro DB1, the mean accuracy achieves 86.16%, showcasing a 13.25% improvement over the best-performing traditional decoding method. Similarly, in ISRMyo-I, a mean accuracy of 70.41% is attained, signifying a 7.4% increase in accuracy compared to traditional methods.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This research establishes a robust framework capable of mitigating cross-user differences in hand gesture recognition based on sEMG signals. By employing deep transfer learning techniques and multi-feature image processing, the study significantly enhances the accuracy of cross-subject hand gesture recognition. This advancement holds promise for enriching human–machine interaction and extending the practical applications of this technology in assisting disabled individuals and robotics.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":" 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138492502","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

Nanotechnology Potent Photothermal and Photodynamic Immunotherapies of Cancer 纳米技术有效的光热和光动力免疫治疗癌症

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-11-10 DOI: 10.1007/s40846-023-00836-6

Abduladheem Turki Jalil, Noora. G. Al-Fatlawi, Lubna R. Al-Ameer, Mustafa Asaad Rasol, Jabbar Hassoon Zamil Al-Tamimi, Muna S. Merza, Ronak Taher Ali, Rahman S. Zabibah, Farah Al-Hili

引用次数: 0

Practically Feasible Sensor-Embedded Kinetic Assessment Piano System for Quantifying Striking Force of Digits During Piano Playing 实际可行的嵌入式传感器动态评估钢琴系统，用于量化钢琴演奏过程中数字的冲击力

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-11-07 DOI: 10.1007/s40846-023-00835-7

Kuan-Yin Lai, Chieh-Hsiang Hsu, Yu-Chen Lin, Chung-Hung Tsai, Cheng-Feng Lin, Li-Chieh Kuo, Fong-Chin Su

Abstract Purpose Understanding the pathogenesis of playing-related hand disorders through investigations based on coordination and biomechanical perspectives is indispensable. This study aimed to establish a sensor-embedded kinetic assessment piano system (SeKAPS) and conduct reliability and validity tests for this system. In addition, the differences in digit coordination between professional pianists and non-musicians were investigated. Methods Twelve subminiature load cells were embedded in the middle of the 12 corresponding keys of an upright piano. A customized calibrated system with a load cell was used to establish the criterion-related validity of the SeKAPS. The reliability of the SeKAPS was determined by 22 volunteer pianists. The other ten professional pianists and ten non-musicians were recruited to indicate the feasibility of the SeKAPS to distinguish the performing differences between groups. Results The R 2 values of regression analyses for the load cells were 0.993–0.999 ( p < 0.001), indicating high validity. The ICC values of the modified keys were 0.82–0.93, indicating high reliability. The results indicate that SeKAPS is accurate in detecting the striking force of digits during piano playing. Significant differences existed in the mean peak force and mean CVs of the peak force of the specific digits between the two groups. The results showed differences in finger control strategies between the pianists and non-musicians. Conclusion The SeKAPS may provide a valuable assessment for assisting pianists in understanding digit force control and movement strategies to achieve efficient digit coordination.

目的从协调性和生物力学的角度研究游戏相关手部疾病的发病机制是必不可少的。本研究旨在建立传感器嵌入式钢琴动力学评估系统(SeKAPS)，并对该系统进行信度和效度检验。此外，研究了专业钢琴家和非音乐家在手指协调方面的差异。方法在立式钢琴的12个对应琴键中间嵌入12个微型测压元件。使用带有称重传感器的定制校准系统来建立SeKAPS与标准相关的有效性。SeKAPS的可靠性是由22名志愿钢琴家确定的。另外10名专业钢琴家和10名非音乐家被招募来表明SeKAPS区分不同群体之间演奏差异的可行性。结果测压元件回归分析的r2值为0.993 ~ 0.999 (p <0.001)，表明高效度。修改后的密钥ICC值为0.82 ~ 0.93，可靠性较高。结果表明，SeKAPS在钢琴弹奏过程中数字敲击力的检测是准确的。两组在具体指的平均峰值力和平均峰值力cv上存在显著差异。结果显示，钢琴家和非音乐家在手指控制策略上存在差异。结论SeKAPS可以帮助钢琴家了解手指力量控制和运动策略，以达到有效的手指协调。

{"title":"Practically Feasible Sensor-Embedded Kinetic Assessment Piano System for Quantifying Striking Force of Digits During Piano Playing","authors":"Kuan-Yin Lai, Chieh-Hsiang Hsu, Yu-Chen Lin, Chung-Hung Tsai, Cheng-Feng Lin, Li-Chieh Kuo, Fong-Chin Su","doi":"10.1007/s40846-023-00835-7","DOIUrl":"https://doi.org/10.1007/s40846-023-00835-7","url":null,"abstract":"Abstract Purpose Understanding the pathogenesis of playing-related hand disorders through investigations based on coordination and biomechanical perspectives is indispensable. This study aimed to establish a sensor-embedded kinetic assessment piano system (SeKAPS) and conduct reliability and validity tests for this system. In addition, the differences in digit coordination between professional pianists and non-musicians were investigated. Methods Twelve subminiature load cells were embedded in the middle of the 12 corresponding keys of an upright piano. A customized calibrated system with a load cell was used to establish the criterion-related validity of the SeKAPS. The reliability of the SeKAPS was determined by 22 volunteer pianists. The other ten professional pianists and ten non-musicians were recruited to indicate the feasibility of the SeKAPS to distinguish the performing differences between groups. Results The R 2 values of regression analyses for the load cells were 0.993–0.999 ( p < 0.001), indicating high validity. The ICC values of the modified keys were 0.82–0.93, indicating high reliability. The results indicate that SeKAPS is accurate in detecting the striking force of digits during piano playing. Significant differences existed in the mean peak force and mean CVs of the peak force of the specific digits between the two groups. The results showed differences in finger control strategies between the pianists and non-musicians. Conclusion The SeKAPS may provide a valuable assessment for assisting pianists in understanding digit force control and movement strategies to achieve efficient digit coordination.","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":"1 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135432569","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

Accumulated Hepatic Steatosis Grades Predicted by T2*-IDEAL Fat Fraction T2*-IDEAL脂肪分数预测累积肝脂肪变性分级

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-10-31 DOI: 10.1007/s40846-023-00819-7

Chang-Hsien Liu, Hing-Chiu Chang, Yi-Jui Liu, Wei-Ching Lin, Chi-Feng Hsieh, Te-Pao Lin, Cheng-Hsuan Juan, William Tzu-Liang, Chun-Jung Juan

引用次数: 0

Silk Fibroin Based Core-Shell Nanofibers Loaded with ZnO Nanoparticles: An Ideal Candidate for Designing a Medicated Wound Dressing 负载ZnO纳米粒子的丝素基核壳纳米纤维:设计药物伤口敷料的理想候选材料

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-10-30 DOI: 10.1007/s40846-023-00821-z

Sohrab Moradi, Shahram Nazarian, Peiman Brouki Milan, Mohammad Ali Nilforoushzadeh, Jamil Zargan

引用次数: 0

Tissue Engineering for Sciatic Nerve Repair: Review of Methods and Challenges 坐骨神经修复的组织工程:方法综述与挑战

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-10-27 DOI: 10.1007/s40846-023-00833-9

Yasaman Ebrahimi-kia, Parivash Davoudi, Sima Bordbar

引用次数: 0

Fully Automated Detection of Osteoporosis Stage on Panoramic Radiographs Using YOLOv5 Deep Learning Model and Designing a Graphical User Interface 基于YOLOv5深度学习模型及图形用户界面设计的全景x线片骨质疏松分期全自动检测

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-10-27 DOI: 10.1007/s40846-023-00831-x

Muhammet Üsame ÖZİÇ, Melek Tassoker, Fatma Yuce

引用次数: 0

Temporal Encoded Deep Learning Radiomics Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma 时间编码深度学习放射组学模型用于肝细胞癌微血管侵袭的术前预测

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-10-01 DOI: 10.1007/s40846-023-00829-5

Jiahui Hu, Xi Deng, Yukai Pan, Yutao Wang, Wei Jin

引用次数: 0

Basic Aspects of Skin Tissue Engineering: Cells, Biomaterials, Scaffold Fabrication Techniques, and Signaling Factors 皮肤组织工程的基本方面:细胞、生物材料、支架制造技术和信号因子

4区医学 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical and Biological Engineering

Pub Date : 2023-10-01 DOI: 10.1007/s40846-023-00822-y

Maryam Kaviani, Bita Geramizadeh

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Journal of Medical and Biological Engineering

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀