2022 14th Biomedical Engineering International Conference (BMEiCON)最新文献_第4页

Precise Motor Function Monitor for Parkinson Disease using Low Power and Wearable IMU Body Area Network 基于低功耗可穿戴IMU体域网络的帕金森病精确运动功能监测

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012114

W. Chang, K. Liou, Yo-Tsen Liu, K. Wen

{"title":"Precise Motor Function Monitor for Parkinson Disease using Low Power and Wearable IMU Body Area Network","authors":"W. Chang, K. Liou, Yo-Tsen Liu, K. Wen","doi":"10.1109/BMEiCON56653.2022.10012114","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012114","url":null,"abstract":"The Inertial Measurement Unit (IMU) has been widely used in precision movement analysis and evaluation and applied in the diagnosis and treatment of many diseases. Parkinson disease (PD) is the most common neurodegenerative movement disorder with rest tremor, bradykinesia, and rigidity as the cardinal motor manifestations. A novel algorithm system has been derived to detect all the motor examinations of the Unified Parkinson's Disease Rating Scale (UPDRS), of which the accuracy has been verified by high-speed camera system. This system includes three categories of detection parameters: the trajectory parameters, time-frequency parameters, angle parameters. Average accuracy for the detection with IMU can reach to 87%, 90% and 95%, respectively.With 17 patients’ trial tests, it’s observed that there do have certain differences of the movement parameters in between patients and age 20th youth controls. For 3.6 Pronation and Supination, the rotation speed of normal control can be twice of the patients and the deviation of the amplitude can reach to 45 degrees of patient in comparison to 5 degrees of normal control. Also, for low power and wearable requirements, this processing system have been designed with chip solution and be implemented with TSMC 0.18 mm CMOS process. The power consumption is 0.3713mW and the chip area is 4.2mm by 4.2mm which will be well suited to wearable applications.Our results showed that this processing system could precisely measure the temporal patterns of speed and amplitude decay of the movements, and successfully capture the severity and difference of bradykinesia and poor coordination of the patients of PD.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Depression Audio Data by Deep Learning 基于深度学习的抑郁症音频数据分类

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012102

Phanomkorn Homsiang, T. Treebupachatsakul, Komsan Kiatrungrit, Suvit Poomrittigul

引用次数: 0

A compact water loaded choke configurations for intracavitary microwave hyperthermia 一种用于腔内微波热疗的紧凑的水负载扼流圈配置

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012117

K. Shabeeb Ahamed, K. Arunachalam

引用次数: 0

Attempts at Enhancing eVision’s Influenza Forecasting Using Social Media 利用社交媒体增强eVision流感预测的尝试

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012095

Navid Shaghaghi, Yash Kamdar, Ron Huang, A. Calle, Jaidev Mirchandani, Michael Castillo

{"title":"Attempts at Enhancing eVision’s Influenza Forecasting Using Social Media","authors":"Navid Shaghaghi, Yash Kamdar, Ron Huang, A. Calle, Jaidev Mirchandani, Michael Castillo","doi":"10.1109/BMEiCON56653.2022.10012095","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012095","url":null,"abstract":"Prediction of the spread of infectious diseases such as the seasonal Influenza is of utmost importance in the preparation for and mitigation of the severity of their impact. eVision (short for Epidemic Vision) is a machine learning time series forecaster under research and development by Santa Clara University’s EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation & Design laboratories. Since eVision’s Long Short-Term Memory (LSTM) neural network makes use of Influenza related keywords in Google Trends as prediction features, it stands to reason that further feature selection from trending keywords relating to the flu in social media posts could enhance its prediction. After close examination, the only social media platforms that prove capable of supplying relevant data for time series analysis are the Twitter micro-blogging and Reddit social news aggregation and discussion forum platforms; as other social media platforms are either meant for sharing images and videos, or private multi-cast communication rather than public broadcasting and discourse. However, due to the burstiness of flu related Reddit posts, no useful feature for time series forecasting can be extracted from that platform; and Twitter, which has been examined for Influenza forecasting by numerous other researchers with successful results, poses a number of obstacles such as changes in policy as well as placing features behind expensive paywalls through the disabling of existing free APIs. Regardless however, the results obtained by the addition of Twitter data as another feature in eVision’s LSTM resulted in an almost negligible predictive improvement as delineated in this paper.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"601 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123175827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A ventilator circuit for volume control mode 用于音量控制模式的通风机电路

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10011580

A. Sanpanich, N. Komalawardhana, K. Petsarb

{"title":"A ventilator circuit for volume control mode","authors":"A. Sanpanich, N. Komalawardhana, K. Petsarb","doi":"10.1109/BMEiCON56653.2022.10011580","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10011580","url":null,"abstract":"An automatic ventilator is used to treat patient who has abnormality in respiratory system or spontaneous ventilation is not enough to maintain blood oxygen and carbondioxide level in normal level. It functions to provide a fresh gas flow into patient lungs during an inspiration and remove exhaled gas from lungs during an expiration. Ventilator is known as the most complicated equipment in ICU due to a parameter setting, waveform understanding and variation of patient pathological variable under controlled ventilation which affect to ventilator operation. Then, new user always need time to practice and familiar with ventilator. In this paper, we present a simplified ventilator model by using an effective simulation tools in order to use as a simple tool in ventilation parameter study. The proposed ventilator simulation is basically based on volume control ventilation mode (VCV) with focusing on PEEP setting at expiratory module. We also simulated an operation of O2 concentrator in gas supply module which is designed by using parallel flow system of both air and oxygen. As a preliminary, all main ventilation waveforms $(mathrm{P}_{aw}, mathrm{V}_{T}$, $dot{V}, mathrm{T}_{P}$, PEEP, O2%) obtain from this modified model show an effective response and be able to use as a routine practice for new practitioner. For further study, another basic ventilation mode and setting as PCV, IMV even patient triggering setting will be added in the future.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125052810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint space narrowing progression quantification with joint angle correction in rheumatoid arthritis 类风湿关节炎关节间隙狭窄进展量化与关节角度矫正

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012092

Yafei Ou, P. Ambalathankandy, Ryunosuke Furuya, Seiya Kawada, Tamotsu Kamishima, M. Ikebe

{"title":"Joint space narrowing progression quantification with joint angle correction in rheumatoid arthritis","authors":"Yafei Ou, P. Ambalathankandy, Ryunosuke Furuya, Seiya Kawada, Tamotsu Kamishima, M. Ikebe","doi":"10.1109/BMEiCON56653.2022.10012092","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012092","url":null,"abstract":"Rheumatoid arthritis is a form of autoimmune disease characterized by synovitis that can ultimately cause joint deformities and impaired functioning. The cartilage destruction is one of the most important indicators for diagnosis and treatment of Rheumatoid arthritis, and it is radiographically manifested as joint space narrowing. In this study, we propose a joint location detection method and a sub-pixel accurate method for quantifying joint space narrowing progression with a joint angle correction. The proposed joint location detection method can detect the location of 14 joints from a given hand radiographic image, the error of 89.13% joints is less than 3 pixels (spatial resolution: 0.175 mm/pixel). In our previous works, we measured joint space narrowing progression between a baseline and its follow-up finger joint images by using partial image phase only correlation. We found that the inconsistency of joint angles may lead to characteristic mismatch and thus affect the accuracy of joint space narrowing quantification. In this work, we introduce rotation invariant phase only correlation in joint space narrowing quantification for joint angle correction. In our experiment, the improved quantification method can effectively manage the mismatch due to the inconsistency of joint angles.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127389034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A control system in a micro cone-beam CT machine 微型锥束CT机的控制系统

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012087

Atthasak Kiang-Ia, Sathid Rukkhong, T. Srivongsa, Kittipong Kasantikul, Chalinee Thanasupsombat, S. Aootaphao, S. Thongvigitmanee

{"title":"A control system in a micro cone-beam CT machine","authors":"Atthasak Kiang-Ia, Sathid Rukkhong, T. Srivongsa, Kittipong Kasantikul, Chalinee Thanasupsombat, S. Aootaphao, S. Thongvigitmanee","doi":"10.1109/BMEiCON56653.2022.10012087","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012087","url":null,"abstract":"Images data from the micro cone-beam computed tomography (CBCT) are acquired from a rotation of an object located between an X-ray generator and a flat panel detector; therefore, the rotational position of the motor is very important for image quality of 3D cross-section images. This study focuses on designing the position control of the stepping motor using the motion module, which enhances the control 4-axis motor’s efficiency to optimize and increase the accuracy of motor movement. We designed the stepping motor position control system to control the movement of the micro CBCT system to perform ten circular rotations in a single scanning process. A phantom was used to verify the rotational image accuracy by considering the image at the same position each round. Comparison of the motor movement with and without the motion module showed slight differences of projection images causing artifacts in cross-section images. Thus, the design of the rotation position control using the motion module circuit yielded good performance in terms of precision and rotational accuracy on the CBCT.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130575840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparison Between Wavelet Scattering Transform and Transfer Learning for Elevated Blood Pressure Detection 小波散射变换与迁移学习在高血压检测中的比较

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012088

E. Martinez-Ríos, L. Montesinos, Mariel Alfaro

引用次数: 2

Printability and cytotoxicity of alginate/agarose hydrogel with carboxylmethyl cellulose and apple powder 羧甲基纤维素和苹果粉混合海藻酸盐/琼脂糖水凝胶的印刷性和细胞毒性

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012110

K. Chessadangkul, N. Damrongplasit, S. Morakul, T. Tharasanit, A. Pimpin

引用次数: 0

Real-Time Epilepsy Detection with IMU and Low Power Processor Design 基于IMU和低功耗处理器的实时癫痫检测设计

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012112

Yu-Ju Su, K. Wen, M. Cheng, Chen-Nen Chang

引用次数: 0