Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478591
Maziyar Askari, Wei Chen, Francis Papay, Yang Liu
The wide adoption of fluorescence imaging in various surgical subspecialties has led to the rapid development of fluorescence-guided surgery (FGS) in recent years. Due to the intrinsic nature of Near-Infrared imaging as a functional imaging modality, structural features and details are often degraded or largely lacking in NIR fluorescence images. Accurate and robust registration of color and fluorescence imageries is key for integrated functional/structural surgical guidance. In this study, we have demonstrated the feasibility of dynamic distance-aware RGB-fluorescence image registration across different working distances. Different from conventional feature-based registration methods, our method does not rely on mutual features across different optical imaging modalities. Compared to conventional optics-based methods, the proposed system facilitates wearable imaging systems and handheld imaging applications in surgical settings. We have demonstrated the potential of this method in intraoperative imaging in a biological model.
{"title":"Intraoperative Optical Imaging with Distance-Aware RGB-Fluorescence Image Registration","authors":"Maziyar Askari, Wei Chen, Francis Papay, Yang Liu","doi":"10.1109/MeMeA52024.2021.9478591","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478591","url":null,"abstract":"The wide adoption of fluorescence imaging in various surgical subspecialties has led to the rapid development of fluorescence-guided surgery (FGS) in recent years. Due to the intrinsic nature of Near-Infrared imaging as a functional imaging modality, structural features and details are often degraded or largely lacking in NIR fluorescence images. Accurate and robust registration of color and fluorescence imageries is key for integrated functional/structural surgical guidance. In this study, we have demonstrated the feasibility of dynamic distance-aware RGB-fluorescence image registration across different working distances. Different from conventional feature-based registration methods, our method does not rely on mutual features across different optical imaging modalities. Compared to conventional optics-based methods, the proposed system facilitates wearable imaging systems and handheld imaging applications in surgical settings. We have demonstrated the potential of this method in intraoperative imaging in a biological model.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114629794","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478711
M. Scarpetta, M. Spadavecchia, G. Andria, M. Ragolia, N. Giaquinto
This paper explores the use of a common smartphone for measuring simultaneously both heartbeat intervals and respiratory cycles. The proposed technique uses the smartphone’s accelerometer to measure the seismocardiographic signal and the acceleration due to breathing movements. The measurement is carried out while the subject is laying down, with the smartphone placed on his/her xiphoid process. In the paper, processing algorithms are presented, that can be used to obtain the heartbeat and the respiratory intervals from the measured signals. As concrete examples of possible application, heartbeat intervals are used to derive Heart Rate Variability and, together with the respiratory signal, to derive a Respiratory Sinus Arrhythmia measure on eight healthy volunteers.
{"title":"Simultaneous Measurement of Heartbeat Intervals and Respiratory Signal using a Smartphone","authors":"M. Scarpetta, M. Spadavecchia, G. Andria, M. Ragolia, N. Giaquinto","doi":"10.1109/MeMeA52024.2021.9478711","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478711","url":null,"abstract":"This paper explores the use of a common smartphone for measuring simultaneously both heartbeat intervals and respiratory cycles. The proposed technique uses the smartphone’s accelerometer to measure the seismocardiographic signal and the acceleration due to breathing movements. The measurement is carried out while the subject is laying down, with the smartphone placed on his/her xiphoid process. In the paper, processing algorithms are presented, that can be used to obtain the heartbeat and the respiratory intervals from the measured signals. As concrete examples of possible application, heartbeat intervals are used to derive Heart Rate Variability and, together with the respiratory signal, to derive a Respiratory Sinus Arrhythmia measure on eight healthy volunteers.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114789561","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478747
G. Giorgi, Nicolò Lago, Sarah Tonello, Alessandra Galli, M. Buonuomo, M. G. Pedersen, A. Cester
Electrolyte-gated organic field-effect transistors (EGOFETs) have been recently investigated as a flexible and low-cost solution for the recording of cellular activity. In particular, electrical pulses, called action potentials (APs), generated by neurons, cause a variation in the source-drain current of an EGOFET. In this paper we propose a method which allows detecting the generation of one or more APs when a given cell is stimulated through the injection of a current pulse. The proposed algorithm is based on three steps: denoising, event detection and event classification. The attention, in this paper, has been principally focused on the design of a suitable denoising algorithm which represents the first fundamental step in the development of an APs detection algorithm. Results reported in this paper show that the Empirical Mode Decomposition (EMD) represents a suitable solution which allows removing noise and, at the same time, keep low the number of eligible events.
{"title":"RL-EGOFET cell biosensors: A novel approach for the detection of action potentials","authors":"G. Giorgi, Nicolò Lago, Sarah Tonello, Alessandra Galli, M. Buonuomo, M. G. Pedersen, A. Cester","doi":"10.1109/MeMeA52024.2021.9478747","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478747","url":null,"abstract":"Electrolyte-gated organic field-effect transistors (EGOFETs) have been recently investigated as a flexible and low-cost solution for the recording of cellular activity. In particular, electrical pulses, called action potentials (APs), generated by neurons, cause a variation in the source-drain current of an EGOFET. In this paper we propose a method which allows detecting the generation of one or more APs when a given cell is stimulated through the injection of a current pulse. The proposed algorithm is based on three steps: denoising, event detection and event classification. The attention, in this paper, has been principally focused on the design of a suitable denoising algorithm which represents the first fundamental step in the development of an APs detection algorithm. Results reported in this paper show that the Empirical Mode Decomposition (EMD) represents a suitable solution which allows removing noise and, at the same time, keep low the number of eligible events.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124015412","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478673
Grazia Iadarola, A. Poli, S. Spinsante
This paper evaluates the Galvanic Skin Response (GSR) signals to three different acoustic stimuli, collected through a commercial wearable device (Empatica E4) by a group of healthy individuals at rest. The collected GSR signals are analyzed depending on the overall number of peaks in the time domain, as well as on the Power Spectral Density (PSD) in the frequency domain, where three bands of interest are identified. In particular, the proposed paper aims to highlight features related to acoustic stimulation. The outcomes show that the GSR signal presents a higher number of GSR peaks in case of unpleasant and neutral stimuli than in case of pleasant stimulus. Moreover, a larger band than the bands typically considered in literature should be observed in the frequency domain, in order to include meaningful PSD of the GSR signal.
{"title":"Analysis of Galvanic Skin Response to Acoustic Stimuli by Wearable Devices","authors":"Grazia Iadarola, A. Poli, S. Spinsante","doi":"10.1109/MeMeA52024.2021.9478673","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478673","url":null,"abstract":"This paper evaluates the Galvanic Skin Response (GSR) signals to three different acoustic stimuli, collected through a commercial wearable device (Empatica E4) by a group of healthy individuals at rest. The collected GSR signals are analyzed depending on the overall number of peaks in the time domain, as well as on the Power Spectral Density (PSD) in the frequency domain, where three bands of interest are identified. In particular, the proposed paper aims to highlight features related to acoustic stimulation. The outcomes show that the GSR signal presents a higher number of GSR peaks in case of unpleasant and neutral stimuli than in case of pleasant stimulus. Moreover, a larger band than the bands typically considered in literature should be observed in the frequency domain, in order to include meaningful PSD of the GSR signal.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124021450","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478701
M. G. Naón, S. Marelli, A. P. Moorhead, B. Saggin, G. Moschioni, M. Tarabini
A compact and light-weight set-up to impose medio-lateral vibration while walking has been designed and manufactured. A vibrating plate was actuated by a motor and a linear guide. After the design and finite-element analysis, the set-up has been manufactured and tested in the frequency band 0.5-4 Hz with amplitude below ± 20 mm, that is compatible for human testing. Total harmonic distortion has been measured below −30 dB. Crosstalk values along Z and Y measured at the corners of the moving platform were lower than 0.1 at 4 Hz. Given the previous analyses, the set-up can be used to test in a movement laboratory the effect of vibration during walking, mounting a walking pad on the platform.
{"title":"Development of a device to impose medio-lateral whole-body vibration while walking","authors":"M. G. Naón, S. Marelli, A. P. Moorhead, B. Saggin, G. Moschioni, M. Tarabini","doi":"10.1109/MeMeA52024.2021.9478701","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478701","url":null,"abstract":"A compact and light-weight set-up to impose medio-lateral vibration while walking has been designed and manufactured. A vibrating plate was actuated by a motor and a linear guide. After the design and finite-element analysis, the set-up has been manufactured and tested in the frequency band 0.5-4 Hz with amplitude below ± 20 mm, that is compatible for human testing. Total harmonic distortion has been measured below −30 dB. Crosstalk values along Z and Y measured at the corners of the moving platform were lower than 0.1 at 4 Hz. Given the previous analyses, the set-up can be used to test in a movement laboratory the effect of vibration during walking, mounting a walking pad on the platform.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124064362","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478737
P. Nabeel, V. R. Kiran, M. Shah, V. AbhidevV., Rahul Manoj, M. Sivaprakasam, J. Joseph
The stiffness of large arteries, measured locally from a small segment or regionally over a long trajectory, has a highly clinically relevant role in cardiovascular hemodynamics. A comprehensive measure of vascular stiffness accounting for both the local and regional stiffness indices has strong potential in stratifying risks of future events. Existing technologies are not amenable for such combined measurements, especially with provisions for easy-to-use, minimal operator dependency, portability, and field deployability. In this work, we report a novel device with these features that perform simultaneous measurement of local and regional stiffness indices. The device uses a single-element ultrasound transducer to measure carotid diameter waveforms in an image-free manner. It estimates the carotid local stiffness indices such as stiffness index (β), pressure-strain elastic modulus (EP), and one-point local pulse wave velocity (PWVβ). A bladder-type thigh cuff enabled the synchronized acquisition of femoral pressure pulse wave, and was used to measure the carotid-femoral pulse wave velocity (cfPWV) – the gold-standard regional aortic stiffness index. An in-vivo study on 35 subjects verified the functionality and measurement reliability of the ARTSENS®. The measured beat-by-beat carotid β (range: 2.71 – 11.15), EP (range: 32.31 – 153.65 kPa), and PWVβ (range: 3.50 – 7.72 m/s) were repeatable with variability < 8.7%. The cfPWV measurements were in agreement with that provided by SphygmoCor device (R = 0.93, p < 0.001, and mean absolute error = 4.82%). The association between local and regional stiffness indices was further investigated. This study demonstrated a strong potential of using ARTSENS® to easily evaluate local and regional stiffness for screening in clinical and resource-constrained settings.
{"title":"An Image-Free Ultrasound Device for Simultaneous Measurement of Local and Regional Arterial Stiffness Indices","authors":"P. Nabeel, V. R. Kiran, M. Shah, V. AbhidevV., Rahul Manoj, M. Sivaprakasam, J. Joseph","doi":"10.1109/MeMeA52024.2021.9478737","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478737","url":null,"abstract":"The stiffness of large arteries, measured locally from a small segment or regionally over a long trajectory, has a highly clinically relevant role in cardiovascular hemodynamics. A comprehensive measure of vascular stiffness accounting for both the local and regional stiffness indices has strong potential in stratifying risks of future events. Existing technologies are not amenable for such combined measurements, especially with provisions for easy-to-use, minimal operator dependency, portability, and field deployability. In this work, we report a novel device with these features that perform simultaneous measurement of local and regional stiffness indices. The device uses a single-element ultrasound transducer to measure carotid diameter waveforms in an image-free manner. It estimates the carotid local stiffness indices such as stiffness index (β), pressure-strain elastic modulus (EP), and one-point local pulse wave velocity (PWVβ). A bladder-type thigh cuff enabled the synchronized acquisition of femoral pressure pulse wave, and was used to measure the carotid-femoral pulse wave velocity (cfPWV) – the gold-standard regional aortic stiffness index. An in-vivo study on 35 subjects verified the functionality and measurement reliability of the ARTSENS®. The measured beat-by-beat carotid β (range: 2.71 – 11.15), EP (range: 32.31 – 153.65 kPa), and PWVβ (range: 3.50 – 7.72 m/s) were repeatable with variability < 8.7%. The cfPWV measurements were in agreement with that provided by SphygmoCor device (R = 0.93, p < 0.001, and mean absolute error = 4.82%). The association between local and regional stiffness indices was further investigated. This study demonstrated a strong potential of using ARTSENS® to easily evaluate local and regional stiffness for screening in clinical and resource-constrained settings.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122501790","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478728
Madison Cohen-McFarlane, P. Xi, Bruce Wallace, J. J. Valdés, R. Goubran, F. Knoefel
In light of the current COVID-19 pandemic response, researchers around the world have been evaluating ways to support all aspects of disease identification, monitoring and tracking. The idea of using audio-based processing methods to evaluate cough events, one of the most common symptoms of COVID-19, in terms of their frequency, severity and characterization has become a promising possible solution. In addition to physical distancing measures, the vast majority of the health authority also recommends the adoption of face coverings (i.e. masks) while in the presence of others and covering one’s cough with a bent elbow. The covering of cough events may present an issue when evaluating recordings using pre-existing cough analysis tools. This paper presents a modeling approach used to characterize the effects of both coughing while wearing a mask and coughing into a bent elbow. These two models were then applied to an existing dataset for evaluating the influence of the face coverings on selected data features that have been used for differentiating wet and dry cough types. It was found that one of the features (number of peaks in the energy spectrum) did not change after mask and elbow modeling, however the second feature (power ratio) was greatly affected and was unable to differentiate between the cough types. The application of these models are therefore recommended when using classification tools that were designed using uncovered clear cough sounds in order to ensure that they will be robust to the presence of face coverings.
{"title":"Impact of face coverings on cough measurement characterization","authors":"Madison Cohen-McFarlane, P. Xi, Bruce Wallace, J. J. Valdés, R. Goubran, F. Knoefel","doi":"10.1109/MeMeA52024.2021.9478728","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478728","url":null,"abstract":"In light of the current COVID-19 pandemic response, researchers around the world have been evaluating ways to support all aspects of disease identification, monitoring and tracking. The idea of using audio-based processing methods to evaluate cough events, one of the most common symptoms of COVID-19, in terms of their frequency, severity and characterization has become a promising possible solution. In addition to physical distancing measures, the vast majority of the health authority also recommends the adoption of face coverings (i.e. masks) while in the presence of others and covering one’s cough with a bent elbow. The covering of cough events may present an issue when evaluating recordings using pre-existing cough analysis tools. This paper presents a modeling approach used to characterize the effects of both coughing while wearing a mask and coughing into a bent elbow. These two models were then applied to an existing dataset for evaluating the influence of the face coverings on selected data features that have been used for differentiating wet and dry cough types. It was found that one of the features (number of peaks in the energy spectrum) did not change after mask and elbow modeling, however the second feature (power ratio) was greatly affected and was unable to differentiate between the cough types. The application of these models are therefore recommended when using classification tools that were designed using uncovered clear cough sounds in order to ensure that they will be robust to the presence of face coverings.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122557990","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478691
Gazi Abdur Rakib, M. S. Islam, Mohamed Abdur Rahman, Syed Maruf Abdullah, M. S. Hossain, N. Alrajeh, Abdulmotaleb El Saddik
In order to flatten the curve and lower human-to-human transmission of COVID-19 pathogen, one of the critical suggestions by health professionals is to monitor COVID-19 virus status of each human dynamically which is not a pragmatic solution unless the COVID-19 positive, negative, or symptomatic subjects are identified and have a secure health certificate generated based on daily health status. In this paper, we have developed a Blockchain and off-chain based secure health status and user biometric storage system. The health status is being visualized through a distributed QR code app. We have also incorporated deep learning-based face recognition and QR code recognition system in which the facial features are mapped to the QR code of a subject. We have developed three distributed apps (dApps): for the citizens, hospital authorities, and COVID-19 status checking entities. The system allows, for example, supermarkets, malls, and airports, to inquire about the health status of any subject through our developed application using already installed cameras. Our system will allow full life-cycle of the health certificate and biometric user management: creation through dApps, secure storage at Blockchain and off-chain, privacy-preserving sharing with the community of interest, and dynamic visualization.
{"title":"DeepHealth: A Secure Framework to Manage Health Certificates Through Medical IoT, Blockchain and Deep Learning","authors":"Gazi Abdur Rakib, M. S. Islam, Mohamed Abdur Rahman, Syed Maruf Abdullah, M. S. Hossain, N. Alrajeh, Abdulmotaleb El Saddik","doi":"10.1109/MeMeA52024.2021.9478691","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478691","url":null,"abstract":"In order to flatten the curve and lower human-to-human transmission of COVID-19 pathogen, one of the critical suggestions by health professionals is to monitor COVID-19 virus status of each human dynamically which is not a pragmatic solution unless the COVID-19 positive, negative, or symptomatic subjects are identified and have a secure health certificate generated based on daily health status. In this paper, we have developed a Blockchain and off-chain based secure health status and user biometric storage system. The health status is being visualized through a distributed QR code app. We have also incorporated deep learning-based face recognition and QR code recognition system in which the facial features are mapped to the QR code of a subject. We have developed three distributed apps (dApps): for the citizens, hospital authorities, and COVID-19 status checking entities. The system allows, for example, supermarkets, malls, and airports, to inquire about the health status of any subject through our developed application using already installed cameras. Our system will allow full life-cycle of the health certificate and biometric user management: creation through dApps, secure storage at Blockchain and off-chain, privacy-preserving sharing with the community of interest, and dynamic visualization.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122818071","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478738
Gregorio Dotti, M. Ghislieri, S. Rosati, V. Agostini, M. Knaflitz, G. Balestra
The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been recently proposed to cope with the high intra-subject variability of muscle activation patterns and to allow the extraction of principal activations (PAs), defined as those muscle activation intervals that are strictly necessary to perform a specific task. To assess differences between different PAs, gait cycle normalization techniques are needed to handle between- and within-subject variability. The aim of this contribution is to assess the effect of two different time-normalization techniques (Linear Length Normalization and Piecewise Linear Length Normalization) on PA extraction, in terms of inter-subject similarity. Results demonstrated no statistically significant differences in the inter-subject similarity between the two tested approaches, revealing, on the average, inter-subject similarity values higher than 0.64. Moreover, a statistically significant difference in the inter-subject similarity among muscles was assessed, revealing a higher similarity of PAs extracted considering the distal lower limb muscles. In conclusion, our results demonstrated that PAs extracted from healthy subjects during a walking task at comfortable walking speed are not affected by the time-normalization approach implemented.
{"title":"Influence of Gait Cycle Normalization on Principal Activations","authors":"Gregorio Dotti, M. Ghislieri, S. Rosati, V. Agostini, M. Knaflitz, G. Balestra","doi":"10.1109/MeMeA52024.2021.9478738","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478738","url":null,"abstract":"The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been recently proposed to cope with the high intra-subject variability of muscle activation patterns and to allow the extraction of principal activations (PAs), defined as those muscle activation intervals that are strictly necessary to perform a specific task. To assess differences between different PAs, gait cycle normalization techniques are needed to handle between- and within-subject variability. The aim of this contribution is to assess the effect of two different time-normalization techniques (Linear Length Normalization and Piecewise Linear Length Normalization) on PA extraction, in terms of inter-subject similarity. Results demonstrated no statistically significant differences in the inter-subject similarity between the two tested approaches, revealing, on the average, inter-subject similarity values higher than 0.64. Moreover, a statistically significant difference in the inter-subject similarity among muscles was assessed, revealing a higher similarity of PAs extracted considering the distal lower limb muscles. In conclusion, our results demonstrated that PAs extracted from healthy subjects during a walking task at comfortable walking speed are not affected by the time-normalization approach implemented.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122152784","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478712
R. Selzler, A. Chan, J. Green
We present the Time Series Event Annotator (TSEA), a graphical user interface annotation tool for time series data that enables rapid visualization, labeling, and annotation of signals, including individual points and ranges. Time series data are common to a variety of applications. Oftentimes there is a need to label segments and/or points of the signals, highlighting important elements that are later used for feature extraction or for signal analysis. A number of illustrative applications of the developed tool are discussed, particularly for the detection of "R" peaks from electrocardiogram signals. While algorithms for detection of "R" peaks can achieve good results when applied to an electrocardiogram signal with a high signal-to-noise ratio, they often lead to incorrect detections in the presence of noise or motion artifact commonly found in clinical setups. In such cases, the Time Series Event Annotator (TSEA) enables efficient imputing of missed or incorrect "R" peak detections, leading to increased data integrity for downstream analysis, at minimum cost. Considering that data cleaning often represents the majority of effort when developing a new machine learning pipeline, our annotation tool will accelerate the development of a wide range of new machine learning applications.
我们介绍了时间序列事件注释器(TSEA)，这是一个用于时间序列数据的图形用户界面注释工具，可以快速可视化、标记和注释信号，包括单个点和范围。时间序列数据在各种应用程序中都很常见。通常需要标记信号的片段和/或点，突出显示稍后用于特征提取或信号分析的重要元素。讨论了所开发工具的一些说明性应用，特别是用于检测心电图信号的“R”峰。虽然检测“R”峰的算法在应用于具有高信噪比的心电图信号时可以取得良好的结果，但在临床设置中常见的噪声或运动伪影存在时，它们通常会导致错误的检测。在这种情况下，时间序列事件注释器(Time Series Event Annotator, TSEA)能够有效地输入缺失或不正确的“R”峰值检测，从而以最小的成本提高下游分析的数据完整性。考虑到在开发新的机器学习管道时，数据清理通常代表了大部分工作，我们的注释工具将加速广泛的新机器学习应用程序的开发。
{"title":"TSEA: An Open Source Python-Based Annotation Tool for Time Series Data","authors":"R. Selzler, A. Chan, J. Green","doi":"10.1109/MeMeA52024.2021.9478712","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478712","url":null,"abstract":"We present the Time Series Event Annotator (TSEA), a graphical user interface annotation tool for time series data that enables rapid visualization, labeling, and annotation of signals, including individual points and ranges. Time series data are common to a variety of applications. Oftentimes there is a need to label segments and/or points of the signals, highlighting important elements that are later used for feature extraction or for signal analysis. A number of illustrative applications of the developed tool are discussed, particularly for the detection of \"R\" peaks from electrocardiogram signals. While algorithms for detection of \"R\" peaks can achieve good results when applied to an electrocardiogram signal with a high signal-to-noise ratio, they often lead to incorrect detections in the presence of noise or motion artifact commonly found in clinical setups. In such cases, the Time Series Event Annotator (TSEA) enables efficient imputing of missed or incorrect \"R\" peak detections, leading to increased data integrity for downstream analysis, at minimum cost. Considering that data cleaning often represents the majority of effort when developing a new machine learning pipeline, our annotation tool will accelerate the development of a wide range of new machine learning applications.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"2019 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128069994","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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