Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856449
Zein Hajj-Ali, K. Greenwood, J. Harrold, J. Green
Newborn patients in the neonatal intensive care unit (NICU) require continuous monitoring of vital signs. Non-contact patient monitoring is preferred in this setting, due to fragile condition of neonatal patients. Depth-based approaches for estimating the respiratory rate (RR) can operate effectively in conditions where an RGB-based method would typically fail, such as low-lighting or where a patient is covered with blankets. Many previously developed depth-based RR estimation techniques require careful camera placement with known geometry relative to the patient, or manual definition of a region of interest (ROI). We here present a framework for depth-based RR estimation where the camera position is arbitrary and the ROI is determined automatically and directly from the depth data. Camera placement is addressed through perspective transformation of the scene, which is accomplished by selecting a small number of registration points known to lie in the same plane. The chest ROI is determined automatically from examining the morphology of progressive depth slices in the corrected depth data. We demonstrate the effectiveness of this RR estimation pipeline using actual neonatal patient depth data collected from an RGB-D sensor. RR estimation accuracy is measured relative to gold standard RR captured from the bedside patient monitor. Perspective transformation is shown to be critical to effectively achieve automated ROI segmentation algorithm. Furthermore, the automated ROI segmentation algorithm is shown to improve both time- and frequency-domain based RR estimation accuracy. When combined, these pre-processing stages are shown to substantially improve the depth-based RR estimation pipeline, with a percentage of acceptable estimates (where the mean absolute error is less than 5 breaths per minute) increasing from 3.60% to 13.47% in the frequency domain and 6.12% to 8.97% in the time domain. Further development will focus on RR estimation from the perspective-corrected depth data and segmented ROI.
{"title":"Towards Depth-based Respiratory Rate Estimation with Arbitrary Camera Placement","authors":"Zein Hajj-Ali, K. Greenwood, J. Harrold, J. Green","doi":"10.1109/MeMeA54994.2022.9856449","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856449","url":null,"abstract":"Newborn patients in the neonatal intensive care unit (NICU) require continuous monitoring of vital signs. Non-contact patient monitoring is preferred in this setting, due to fragile condition of neonatal patients. Depth-based approaches for estimating the respiratory rate (RR) can operate effectively in conditions where an RGB-based method would typically fail, such as low-lighting or where a patient is covered with blankets. Many previously developed depth-based RR estimation techniques require careful camera placement with known geometry relative to the patient, or manual definition of a region of interest (ROI). We here present a framework for depth-based RR estimation where the camera position is arbitrary and the ROI is determined automatically and directly from the depth data. Camera placement is addressed through perspective transformation of the scene, which is accomplished by selecting a small number of registration points known to lie in the same plane. The chest ROI is determined automatically from examining the morphology of progressive depth slices in the corrected depth data. We demonstrate the effectiveness of this RR estimation pipeline using actual neonatal patient depth data collected from an RGB-D sensor. RR estimation accuracy is measured relative to gold standard RR captured from the bedside patient monitor. Perspective transformation is shown to be critical to effectively achieve automated ROI segmentation algorithm. Furthermore, the automated ROI segmentation algorithm is shown to improve both time- and frequency-domain based RR estimation accuracy. When combined, these pre-processing stages are shown to substantially improve the depth-based RR estimation pipeline, with a percentage of acceptable estimates (where the mean absolute error is less than 5 breaths per minute) increasing from 3.60% to 13.47% in the frequency domain and 6.12% to 8.97% in the time domain. Further development will focus on RR estimation from the perspective-corrected depth data and segmented ROI.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382170","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}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856539
Navya Rose George, V. RajKiran, P. Nabeel, M. Sivaprakasam, J. Joseph
Jugular Venous Pulse (JVP) has significant clinical importance in the screening and early detection of various cardiovascular anomalies. Although the conventional state-of-the-art B-mode imaging systems can perform reliable acquisition of JVP signals, additional complex computations and system requirements are necessary to process the acquired signals. Most clinical-grade B-mode systems are expensive and bulky, limiting their large-scale field usability. To meet the needs of a portable, easy-to-use, field amenable system, we propose an image-free A-mode ultrasound system for JVP acquisition. In this work, we have investigated the feasibility of performing A-mode JVP acquisition and its diameter measurements' reliability against a reference B-mode imaging system. An in-vivo study was conducted on 25 healthy human volunteers in the 20–30 age group. The A-mode system permitted reliable acquisition of frames with $text{SNR} > 20 text{dB}$, by real-time monitoring of the visual feedback. It was observed that repeatable and reliable pulses that concur with the physiologically expected JVP morphology were captured. The beat-to-beat variability of the Jugular venous (JV) diameter was found to be less than 4%. The linear regression analysis revealed that the diameter measurements by the developed system were strongly correlated to the reference values ($mathrm{r} > 0.85, mathrm{p} < 0.05$). There was no significant bias in the Bland-Altman analysis between the A-mode and reference systems. The study findings indicate that the proposed A-mode system could acquire high-fidelity JVP signals, which can further be processed using intelligent algorithms to predict vascular health. We have observed that the developed system can provide reliable and repeatable measurements of JV diameter and has a potential for large-scale field studies.
{"title":"Jugular Venous Diameter Measurement Using A-Mode Ultrasound: A Feasibility Study","authors":"Navya Rose George, V. RajKiran, P. Nabeel, M. Sivaprakasam, J. Joseph","doi":"10.1109/MeMeA54994.2022.9856539","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856539","url":null,"abstract":"Jugular Venous Pulse (JVP) has significant clinical importance in the screening and early detection of various cardiovascular anomalies. Although the conventional state-of-the-art B-mode imaging systems can perform reliable acquisition of JVP signals, additional complex computations and system requirements are necessary to process the acquired signals. Most clinical-grade B-mode systems are expensive and bulky, limiting their large-scale field usability. To meet the needs of a portable, easy-to-use, field amenable system, we propose an image-free A-mode ultrasound system for JVP acquisition. In this work, we have investigated the feasibility of performing A-mode JVP acquisition and its diameter measurements' reliability against a reference B-mode imaging system. An in-vivo study was conducted on 25 healthy human volunteers in the 20–30 age group. The A-mode system permitted reliable acquisition of frames with $text{SNR} > 20 text{dB}$, by real-time monitoring of the visual feedback. It was observed that repeatable and reliable pulses that concur with the physiologically expected JVP morphology were captured. The beat-to-beat variability of the Jugular venous (JV) diameter was found to be less than 4%. The linear regression analysis revealed that the diameter measurements by the developed system were strongly correlated to the reference values ($mathrm{r} > 0.85, mathrm{p} < 0.05$). There was no significant bias in the Bland-Altman analysis between the A-mode and reference systems. The study findings indicate that the proposed A-mode system could acquire high-fidelity JVP signals, which can further be processed using intelligent algorithms to predict vascular health. We have observed that the developed system can provide reliable and repeatable measurements of JV diameter and has a potential for large-scale field studies.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131611196","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}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856562
H. Durmuş, Emel Çetin Ari, B. Karaböce, M. Seyidov
In this study, we experimentally investigated the effects of temperature and optical power of an LED therapy device within a tissue phantom and on its surface. We attempted to ascertain the effects of LED lights of different colors, such as red, yellow, green, blue, orange, and purple, situated at the LED therapy device on the surface and within the agar phantom. We formed a temperature effect on the agar phantom via the LED therapy device at 20, 40, and 60 minutes intervals. The temperature measurements were performed using a thermocouple placed at the surface and within the agar phantom. Furthermore, the relationships between the obtained internal temperatures of each LED light of different colors and the determined surface temperatures of each LED light of different colors were statistically analyzed, discussed, and evaluated. In addition, as well as to characterize the agar phantom optically and acoustically, optical power measurements were also made under different LED lights at the phantom surface level. This study aimed to investigate the temperature and optical power effects of an LED therapy device on a well-characterized tissue-mimicking phantom prior to clinical application. The results of this study indicate that the LED therapy device examined is safe and harmless for daily use, particularly in terms of temperature and related optical power effects.
{"title":"Experimental Evaluation of Temperature and Optical Power Generated by a LED Therapy Device on an Agar Phantom","authors":"H. Durmuş, Emel Çetin Ari, B. Karaböce, M. Seyidov","doi":"10.1109/MeMeA54994.2022.9856562","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856562","url":null,"abstract":"In this study, we experimentally investigated the effects of temperature and optical power of an LED therapy device within a tissue phantom and on its surface. We attempted to ascertain the effects of LED lights of different colors, such as red, yellow, green, blue, orange, and purple, situated at the LED therapy device on the surface and within the agar phantom. We formed a temperature effect on the agar phantom via the LED therapy device at 20, 40, and 60 minutes intervals. The temperature measurements were performed using a thermocouple placed at the surface and within the agar phantom. Furthermore, the relationships between the obtained internal temperatures of each LED light of different colors and the determined surface temperatures of each LED light of different colors were statistically analyzed, discussed, and evaluated. In addition, as well as to characterize the agar phantom optically and acoustically, optical power measurements were also made under different LED lights at the phantom surface level. This study aimed to investigate the temperature and optical power effects of an LED therapy device on a well-characterized tissue-mimicking phantom prior to clinical application. The results of this study indicate that the LED therapy device examined is safe and harmless for daily use, particularly in terms of temperature and related optical power effects.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115074367","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}
With the increasing risks of cardiovascular diseases (CVDs) all over the world, electrocardiogram (ECG) monitoring has become an important means for the timely diagnosis of CVDs. However, ECG signal can be easily disturbed by noises such as motion artifact (MA) when recorded by wearable devices in our daily life. To eliminate these noises in ECG signal, a denoising algorithm based on multi-threshold stationary wavelet transform (SWT), called MT-SWT, is proposed. We first propose a QRS complex detection algorithm based on joint threshold judgement to accurately separate the QRS complex from the other waves of ECG signals. Then, taking historical ECG signals when the human body is static as the reference signals, we set multiple thresholds for different SWT coefficients and different parts of ECG signals respectively. Finally, for a section of the input ECG signal, each SWT coefficient is processed by a given soft thresholding function for denoising. We compare MT-SWT with other algorithms based on MIT-BIH datasets, and also implement it in real-world ECG monitoring wearable devices. The experimental results show that compared with the state-of-the-arts, MT-SWT achieves higher accuracy on QRS complex detection under the condition of low signal-to-noise ratio (SNR). Moreover, MT-SWT achieves high SNR improvement ($SNR_{imp}$) and low percent root mean square difference ($PRD$) under different SNR conditions.
{"title":"Electrocardiogram Signal Denoising Based on Multi-Threshold Stationary Wavelet Transform","authors":"Huyang Peng, Yongrui Chen, Donglin Shi, Fengling Xie","doi":"10.1109/MeMeA54994.2022.9856544","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856544","url":null,"abstract":"With the increasing risks of cardiovascular diseases (CVDs) all over the world, electrocardiogram (ECG) monitoring has become an important means for the timely diagnosis of CVDs. However, ECG signal can be easily disturbed by noises such as motion artifact (MA) when recorded by wearable devices in our daily life. To eliminate these noises in ECG signal, a denoising algorithm based on multi-threshold stationary wavelet transform (SWT), called MT-SWT, is proposed. We first propose a QRS complex detection algorithm based on joint threshold judgement to accurately separate the QRS complex from the other waves of ECG signals. Then, taking historical ECG signals when the human body is static as the reference signals, we set multiple thresholds for different SWT coefficients and different parts of ECG signals respectively. Finally, for a section of the input ECG signal, each SWT coefficient is processed by a given soft thresholding function for denoising. We compare MT-SWT with other algorithms based on MIT-BIH datasets, and also implement it in real-world ECG monitoring wearable devices. The experimental results show that compared with the state-of-the-arts, MT-SWT achieves higher accuracy on QRS complex detection under the condition of low signal-to-noise ratio (SNR). Moreover, MT-SWT achieves high SNR improvement ($SNR_{imp}$) and low percent root mean square difference ($PRD$) under different SNR conditions.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115365419","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}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856574
F. Amato, Maria Fasani, Glauco Raffaelli, Valerio Cesarini, Gabriella Olmo, N. Lorenzo, G. Costantini, G. Saggio
Automatic assessment of speech disorders is a cutting-edge topic in vocal analysis. Recent studies indicated possible connections between eating disorders and voice alterations. In this work, we assessed the influence of obesity and Gastro- Esophageal Reflux Disease (GERD) on voice, being the former a risk factor for the latter. Moreover, we investigated the mutual influence of the diseases working with a consistent set of features. To these aims, we used vocal tests from 92 subjects, with vocal tests consisting of vowel phonation and sentence repetition, and subjects including healthy controls, obese patients, patients with GERD, and obese patients with GERD. Machine Learning models, consisting of Naive Bayes and Support Vector Machine, were successfully employed on extracted features in binary classifications, resulting in 0.86 and 0.82 of accuracies on validation set in scoring the presence of GERD and obesity, respectively. The absence of performance deterioration when moving to the test set denoted a lack of overfitting. As for the tasks and the features employed, the sentence repetition proved to be more effective than the vowel phonation, while Mel Frequency Cepstral Coefficients, Perceptual Linear Prediction Coefficients, Bark Band Energy Coefficients, and noise measures appear to be among the most significant features for the application at hand.
{"title":"Obesity and Gastro-Esophageal Reflux voice disorders: a Machine Learning approach","authors":"F. Amato, Maria Fasani, Glauco Raffaelli, Valerio Cesarini, Gabriella Olmo, N. Lorenzo, G. Costantini, G. Saggio","doi":"10.1109/MeMeA54994.2022.9856574","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856574","url":null,"abstract":"Automatic assessment of speech disorders is a cutting-edge topic in vocal analysis. Recent studies indicated possible connections between eating disorders and voice alterations. In this work, we assessed the influence of obesity and Gastro- Esophageal Reflux Disease (GERD) on voice, being the former a risk factor for the latter. Moreover, we investigated the mutual influence of the diseases working with a consistent set of features. To these aims, we used vocal tests from 92 subjects, with vocal tests consisting of vowel phonation and sentence repetition, and subjects including healthy controls, obese patients, patients with GERD, and obese patients with GERD. Machine Learning models, consisting of Naive Bayes and Support Vector Machine, were successfully employed on extracted features in binary classifications, resulting in 0.86 and 0.82 of accuracies on validation set in scoring the presence of GERD and obesity, respectively. The absence of performance deterioration when moving to the test set denoted a lack of overfitting. As for the tasks and the features employed, the sentence repetition proved to be more effective than the vowel phonation, while Mel Frequency Cepstral Coefficients, Perceptual Linear Prediction Coefficients, Bark Band Energy Coefficients, and noise measures appear to be among the most significant features for the application at hand.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114555305","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}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856572
A. Patange, Zhihang Zhang, Ruairi Monaghan, M. Fallon, H. Humphreys, B. Tiwari, S. Daniels
Exposure to bioaerosols are associated with wide a range of public health issues. Pathogenic bioaerosols can contribute to the onset of various diseases, therefore their rapid and efficient detection is crucial to public health. Loop Mediated Isothermal Amplification (LAMP) is a highly specific and accurate nucleic acid amplification method to detect microbes. In this study, we developed a simplified LAMP assay capable of detecting microbes in aerosols with minimal chemical and processing requirements. An air sampling system was designed to efficiently collect and recover microbes in aerosols and integrate into a LAMP assay process. We demonstrated successful collection of Escherichia coli (E. coli) aerosols and detection by a colorimetric LAMP assay. It was found that the colorimetric LAMP assay detected E. coli in concentrations as low as 102 CFU/ml. This combined technology enables accurate and rapid genomic detection of bioaerosols outside of conventional laboratory settings. This work describes a fully automated colorimetric LAMP assay device, the Luremain stable for up to 4 weeks at room temperature, however this study is ongoing, and we expect a significantly longer life of the reagent.smAir LM365, for facilitating the integrated technology with easy operation. All the processes including air sampling, DNA extraction, DNA amplification and detection were integrated on this device. The cartridge design allows the device to complete several detection processes before an intervention is required by an operator. We demonstrated that E. coli contaminated water samples can be automatically detected and analysed on our LAMP assay device in approximately 60 min. Along with the automation of the device, stable and long-term storage of LAMP reagents is an important requirement. Here we also comment on a preservation method for the LAMP reagents, and we evaluate the stability of preserved reagents at ambient temperature. Our data indicate that preserved LAMP reagents can remain stable for up to 4 weeks at room temperature, however this study is ongoing, and we expect a significantly longer life of the reagent.
{"title":"Development of an Integrated Air Sampling and Loop-Mediated Isothermal Amplification (LAMP) technology for detection of bioaerosols in indoor environments","authors":"A. Patange, Zhihang Zhang, Ruairi Monaghan, M. Fallon, H. Humphreys, B. Tiwari, S. Daniels","doi":"10.1109/MeMeA54994.2022.9856572","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856572","url":null,"abstract":"Exposure to bioaerosols are associated with wide a range of public health issues. Pathogenic bioaerosols can contribute to the onset of various diseases, therefore their rapid and efficient detection is crucial to public health. Loop Mediated Isothermal Amplification (LAMP) is a highly specific and accurate nucleic acid amplification method to detect microbes. In this study, we developed a simplified LAMP assay capable of detecting microbes in aerosols with minimal chemical and processing requirements. An air sampling system was designed to efficiently collect and recover microbes in aerosols and integrate into a LAMP assay process. We demonstrated successful collection of Escherichia coli (E. coli) aerosols and detection by a colorimetric LAMP assay. It was found that the colorimetric LAMP assay detected E. coli in concentrations as low as 102 CFU/ml. This combined technology enables accurate and rapid genomic detection of bioaerosols outside of conventional laboratory settings. This work describes a fully automated colorimetric LAMP assay device, the Luremain stable for up to 4 weeks at room temperature, however this study is ongoing, and we expect a significantly longer life of the reagent.smAir LM365, for facilitating the integrated technology with easy operation. All the processes including air sampling, DNA extraction, DNA amplification and detection were integrated on this device. The cartridge design allows the device to complete several detection processes before an intervention is required by an operator. We demonstrated that E. coli contaminated water samples can be automatically detected and analysed on our LAMP assay device in approximately 60 min. Along with the automation of the device, stable and long-term storage of LAMP reagents is an important requirement. Here we also comment on a preservation method for the LAMP reagents, and we evaluate the stability of preserved reagents at ambient temperature. Our data indicate that preserved LAMP reagents can remain stable for up to 4 weeks at room temperature, however this study is ongoing, and we expect a significantly longer life of the reagent.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116872613","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}
Pub Date : 2022-06-22DOI: 10.1109/memea54994.2022.9856411
F. Simini, Isabel Morales, Natalia Garay, Darío Santos, Maria Rene Ledezma, Estefania Della Mea, Pablo Sánchez, Lucia Belen Ribeiro
Biomedical equipment has evolved from marginal auxiliaries to become central elements in patient physician relationship of today. Clinical records, once a separate item, become fully integrated as the Electronic Clinical Record with medical notes, images, monitoring, medication, lab results and life style information under the supervision of physicians. Three variants of standard biomedical equipment architecture are derived from twelve original Biomedical Equipment.
{"title":"Standard Classification of Biomedical Equipment According to Measurements, Medical Information and Electronic Clinical Records","authors":"F. Simini, Isabel Morales, Natalia Garay, Darío Santos, Maria Rene Ledezma, Estefania Della Mea, Pablo Sánchez, Lucia Belen Ribeiro","doi":"10.1109/memea54994.2022.9856411","DOIUrl":"https://doi.org/10.1109/memea54994.2022.9856411","url":null,"abstract":"Biomedical equipment has evolved from marginal auxiliaries to become central elements in patient physician relationship of today. Clinical records, once a separate item, become fully integrated as the Electronic Clinical Record with medical notes, images, monitoring, medication, lab results and life style information under the supervision of physicians. Three variants of standard biomedical equipment architecture are derived from twelve original Biomedical Equipment.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115932114","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}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856473
L. Kohout, J. Scheerer, C. Zimmermann, Wilhelm Stork
Accurate camera-based human action recognition over longer periods of time or in different camera views requires re-identification of individuals to correctly associate the actions. This is especially important if you want to track people's actions over time. Most work in person re-identification currently focuses on improving the performance of re-identification models for images of people wearing everyday clothing. This becomes a problem when the re-identification scenario changes, and with it the typical appearance of people in that specific environment. Therefore, this work examines the effects of medical clothing on five different person re-identification algorithms. As artificial intelligence and computer vision find more and more applications in the medical field, the question arises to what extent current implementations of person re-identification algorithms are able to generalize from non-medical data, so that the algorithms can be applied in a medical scenario. Since person re-identification is a well-studied topic in the computer vision community, and can also be used in medical settings, this work focuses on the impact of medical clothing on such algorithms. This becomes relevant because the medical clothing is highly uniform and covers many features of a person's characteristics. In addition to examining the effects of clothing as described, ways to overcome the resulting limitations are discussed. In the absence of medical datasets for person re-identification, a suitable dataset was generated containing images of people in medical clothing and the required annotations. Five different existing re-identification models were trained on a non-medical dataset and then tested with the medical data created for this study. The results show a general drop in performance when subjects are wearing medical clothing instead of normal cloths. By additionally marking all people with individual colored hairnets, the re-identification performance can be improved compared to the unmarked subjects.
{"title":"Effects of Medical Clothing on Person Re-Identification Algorithms","authors":"L. Kohout, J. Scheerer, C. Zimmermann, Wilhelm Stork","doi":"10.1109/MeMeA54994.2022.9856473","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856473","url":null,"abstract":"Accurate camera-based human action recognition over longer periods of time or in different camera views requires re-identification of individuals to correctly associate the actions. This is especially important if you want to track people's actions over time. Most work in person re-identification currently focuses on improving the performance of re-identification models for images of people wearing everyday clothing. This becomes a problem when the re-identification scenario changes, and with it the typical appearance of people in that specific environment. Therefore, this work examines the effects of medical clothing on five different person re-identification algorithms. As artificial intelligence and computer vision find more and more applications in the medical field, the question arises to what extent current implementations of person re-identification algorithms are able to generalize from non-medical data, so that the algorithms can be applied in a medical scenario. Since person re-identification is a well-studied topic in the computer vision community, and can also be used in medical settings, this work focuses on the impact of medical clothing on such algorithms. This becomes relevant because the medical clothing is highly uniform and covers many features of a person's characteristics. In addition to examining the effects of clothing as described, ways to overcome the resulting limitations are discussed. In the absence of medical datasets for person re-identification, a suitable dataset was generated containing images of people in medical clothing and the required annotations. Five different existing re-identification models were trained on a non-medical dataset and then tested with the medical data created for this study. The results show a general drop in performance when subjects are wearing medical clothing instead of normal cloths. By additionally marking all people with individual colored hairnets, the re-identification performance can be improved compared to the unmarked subjects.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124793652","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}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856550
G. Bandini, A. Landi, F. Santini, A. Basolo, M. Marracci, P. Piaggi
Whole-room indirect calorimeters (WRIC) are accurate tools to precisely measure energy metabolism in humans via calculation of oxygen consumption and carbon dioxide production. Yet, overall accuracy of metabolic measurements relies on the validity of the theoretical model for gas exchange inside the WRIC volume in addition to experimental and environmental conditions that contribute to the uncertainty of WRIC outcome variables. The aim of this study was to quantitatively study the static sensitivity of a WRIC operated in a push configuration and located at the laboratories of the University Hospital of Pisa with the goal to identify the experimental conditions required to reach the best degree of accuracy for outcome metabolic measurements. Herein we demonstrate that achieving a fractional concentration of carbon dioxide inside the $text{WRIC} > 0.2{%}$ at the steady state conditions allows to obtain a relative uncertainty <5% for the outcome metabolic measurements.
{"title":"Static sensitivity of whole-room indirect calorimeters","authors":"G. Bandini, A. Landi, F. Santini, A. Basolo, M. Marracci, P. Piaggi","doi":"10.1109/MeMeA54994.2022.9856550","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856550","url":null,"abstract":"Whole-room indirect calorimeters (WRIC) are accurate tools to precisely measure energy metabolism in humans via calculation of oxygen consumption and carbon dioxide production. Yet, overall accuracy of metabolic measurements relies on the validity of the theoretical model for gas exchange inside the WRIC volume in addition to experimental and environmental conditions that contribute to the uncertainty of WRIC outcome variables. The aim of this study was to quantitatively study the static sensitivity of a WRIC operated in a push configuration and located at the laboratories of the University Hospital of Pisa with the goal to identify the experimental conditions required to reach the best degree of accuracy for outcome metabolic measurements. Herein we demonstrate that achieving a fractional concentration of carbon dioxide inside the $text{WRIC} > 0.2{%}$ at the steady state conditions allows to obtain a relative uncertainty <5% for the outcome metabolic measurements.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122849047","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}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856465
F. Nardo, Martina Morano, S. Fioretti
The present study involves Continuous Wavelet Transform (CWT) for the analysis of surface electromyographic (sEM G) signals, with the aim of assessing muscle co-contraction during early stance of healthy-subj ect walking. CWT approach allows computing the coscalogram function, a localized statistical assessment of cross-energy density between two signals. In this study, CWT coscalogram function between two sEMG signals from antagonist muscles is used to quantify muscular co-contraction activity. Daubechies of order 4 (factorization in 6 levels) is adopted as mother wavelet. Noise reduction in the sEMG signals is performed applying CWT denoising. Co-contractions between gastrocnemius lateralis and tibialis anterior are assessed on a set of experimental sEM G signals acquired in 15 able-bodied subjects during walking. Results show as the present CWT approach can provide a reliable assessment of co-contraction in early-stance phase of walking, highlighting that this co-contraction is short (< 1 0 ms) and very frequent. A large variability in the occurrence of the co-contraction is also detected, suggesting that each subject adopts her/his own modality of co-contraction. However, the same physiological purpose is maintained for all subj ects, i.e., to control shock absorption and improve weight-bearing stability during the first phase of human walking. Physiological reliability of experimental results suggests the appropriateness of the present method in clinical applications.
{"title":"Quantification of ankle muscle co-contraction during early stance by wavelet-based analysis of surface electromyographic signals","authors":"F. Nardo, Martina Morano, S. Fioretti","doi":"10.1109/MeMeA54994.2022.9856465","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856465","url":null,"abstract":"The present study involves Continuous Wavelet Transform (CWT) for the analysis of surface electromyographic (sEM G) signals, with the aim of assessing muscle co-contraction during early stance of healthy-subj ect walking. CWT approach allows computing the coscalogram function, a localized statistical assessment of cross-energy density between two signals. In this study, CWT coscalogram function between two sEMG signals from antagonist muscles is used to quantify muscular co-contraction activity. Daubechies of order 4 (factorization in 6 levels) is adopted as mother wavelet. Noise reduction in the sEMG signals is performed applying CWT denoising. Co-contractions between gastrocnemius lateralis and tibialis anterior are assessed on a set of experimental sEM G signals acquired in 15 able-bodied subjects during walking. Results show as the present CWT approach can provide a reliable assessment of co-contraction in early-stance phase of walking, highlighting that this co-contraction is short (< 1 0 ms) and very frequent. A large variability in the occurrence of the co-contraction is also detected, suggesting that each subject adopts her/his own modality of co-contraction. However, the same physiological purpose is maintained for all subj ects, i.e., to control shock absorption and improve weight-bearing stability during the first phase of human walking. Physiological reliability of experimental results suggests the appropriateness of the present method in clinical applications.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126334062","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}
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