Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478767
Bruno Tardif, D. Lo, R. Goubran
Impulsive sounds can cause severe hearing damage and even hearing loss. Sound protection devices are widely used to attenuate impulsive sounds and reduce their impact on hearing. Properly measuring and characterizing the sound attenuation is essential when choosing a specific hearing protection device. Currently, hearing protection devices are often characterized using the Impulse Peak Insertion Loss (IPIL) that measures the total attenuation across all frequency bands. IPIL does not provide any information about the spectral attenuation of the device. Human hearing is spectrally sensitive, and the risk of noise-induced hearing damage is frequency-dependent. Therefore, characterizing hearing protection devices has to be done for both the peak and the full audible frequency spectrum from 20 Hz to 20k Hz. In this paper, we propose a novel energy preserving method for estimating the 1/3 octave band insertion loss using the continuous wavelet transform. To do so, we collected gunshot audio sounds from firing a sniper rifle and evaluated the sound attenuation effect of adding a sound protection device (or sound suppressor) to the rifle. The method that we called Wavelets Octave Band Insertion Loss (WOBIL) is compared with existing methods such as the IPIL, the Impulsive Spectral Insertion Loss (ISIL) and the recently published Octave Band Impulse Peak Insertion Loss (OBIPIL).
{"title":"Measurement and Characterization of Hearing Protection Devices in the Presence of Impulse Sound","authors":"Bruno Tardif, D. Lo, R. Goubran","doi":"10.1109/MeMeA52024.2021.9478767","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478767","url":null,"abstract":"Impulsive sounds can cause severe hearing damage and even hearing loss. Sound protection devices are widely used to attenuate impulsive sounds and reduce their impact on hearing. Properly measuring and characterizing the sound attenuation is essential when choosing a specific hearing protection device. Currently, hearing protection devices are often characterized using the Impulse Peak Insertion Loss (IPIL) that measures the total attenuation across all frequency bands. IPIL does not provide any information about the spectral attenuation of the device. Human hearing is spectrally sensitive, and the risk of noise-induced hearing damage is frequency-dependent. Therefore, characterizing hearing protection devices has to be done for both the peak and the full audible frequency spectrum from 20 Hz to 20k Hz. In this paper, we propose a novel energy preserving method for estimating the 1/3 octave band insertion loss using the continuous wavelet transform. To do so, we collected gunshot audio sounds from firing a sniper rifle and evaluated the sound attenuation effect of adding a sound protection device (or sound suppressor) to the rifle. The method that we called Wavelets Octave Band Insertion Loss (WOBIL) is compared with existing methods such as the IPIL, the Impulsive Spectral Insertion Loss (ISIL) and the recently published Octave Band Impulse Peak Insertion Loss (OBIPIL).","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"67 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":"127569108","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/MeMeA52024.2021.9478776
G. Cesarelli, L. Donisi, G. Caprio, M. Scioli, A. Biancardi, G. D'Addio
The study of posture and gait abnormalities has revealed over years potential information to improve the rehabilitation outcome of several classes of ill patients; nevertheless, this results still an area of research almost completely unexplored in the case of obese patients. Consequently, this study was designed as a preliminary investigation to determine potential statistical correlations between kinematic features and “gold standard” methodologies in the field, e.g., the Western Ontario and Mc Master University scale and the Barthel index. To this aim, physicians prepared bioelectrical impedance analyses and clinical assessments to evaluate patients' clinical scores, while biomedical engineers have organized Instrumented Stand and Walking tests to quantify several kinematic features using a microelectromechanical system equipped by a series of inertial measurement units. Finally, a statistical correlation analysis has allowed to reveal several features – related to patients’ anticipatory postural adjustments movements and gait – demonstrated a mild and moderate correlation with some clinical indices. In conclusion, this paper presents a novel view to address and design innovative rehabilitation strategies for obese patients.
{"title":"Statistical correlation analysis between kinematic features and clinical indexes and scales for obese patients","authors":"G. Cesarelli, L. Donisi, G. Caprio, M. Scioli, A. Biancardi, G. D'Addio","doi":"10.1109/MeMeA52024.2021.9478776","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478776","url":null,"abstract":"The study of posture and gait abnormalities has revealed over years potential information to improve the rehabilitation outcome of several classes of ill patients; nevertheless, this results still an area of research almost completely unexplored in the case of obese patients. Consequently, this study was designed as a preliminary investigation to determine potential statistical correlations between kinematic features and “gold standard” methodologies in the field, e.g., the Western Ontario and Mc Master University scale and the Barthel index. To this aim, physicians prepared bioelectrical impedance analyses and clinical assessments to evaluate patients' clinical scores, while biomedical engineers have organized Instrumented Stand and Walking tests to quantify several kinematic features using a microelectromechanical system equipped by a series of inertial measurement units. Finally, a statistical correlation analysis has allowed to reveal several features – related to patients’ anticipatory postural adjustments movements and gait – demonstrated a mild and moderate correlation with some clinical indices. In conclusion, this paper presents a novel view to address and design innovative rehabilitation strategies for obese patients.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"32 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":"126873538","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/MeMeA52024.2021.9478775
Flavia Forconi, L. Apa, L. D’Alvia, Marianna Cosentino, E. Rizzuto, Z. Prete
Electrical stimulation (ES) highly influences the cellular microenvironment, affecting cell migration, proliferation and differentiation. It also plays a crucial role in tissue engineering to improve the biomechanical properties of the constructs and regenerate the damaged tissues. However, the effects of the ES on the neuromuscular junction (NMJ) are still not fully analyzed. In this context, the development of a specialized microfluidic device combined with an ad-hoc electrical stimulation can allow a better investigation of the NMJ functionality. To this aim, we performed an analysis of the electric field distribution in a 3D neuromuscular junction microfluidic device for the design of several electrode systems. At first, we designed and modeled the 3D microfluidic device in order to promote the formation of the NMJ between neuronal cells and the muscle engineered tissue. Subsequently, with the aim of identifying the optimal electrode configuration able to properly stimulate the neurites, thus enhancing the formation of the NMJ, we performed different simulation tests of the electric field distribution, by varying the electrode type, size, position and applied voltage. Our results revealed that all the tested configurations did not induce an electric field dangerous for the cell vitality. Among these configurations, the one with cylindrical pin of 0.3 mm of radius, placed in the internal position of the neuronal chambers, allowed to obtain the highest electrical field in the zone comprising the neurites.
{"title":"Electric field distribution analysis for the design of an electrode system in a 3D neuromuscular junction microfluidic device","authors":"Flavia Forconi, L. Apa, L. D’Alvia, Marianna Cosentino, E. Rizzuto, Z. Prete","doi":"10.1109/MeMeA52024.2021.9478775","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478775","url":null,"abstract":"Electrical stimulation (ES) highly influences the cellular microenvironment, affecting cell migration, proliferation and differentiation. It also plays a crucial role in tissue engineering to improve the biomechanical properties of the constructs and regenerate the damaged tissues. However, the effects of the ES on the neuromuscular junction (NMJ) are still not fully analyzed. In this context, the development of a specialized microfluidic device combined with an ad-hoc electrical stimulation can allow a better investigation of the NMJ functionality. To this aim, we performed an analysis of the electric field distribution in a 3D neuromuscular junction microfluidic device for the design of several electrode systems. At first, we designed and modeled the 3D microfluidic device in order to promote the formation of the NMJ between neuronal cells and the muscle engineered tissue. Subsequently, with the aim of identifying the optimal electrode configuration able to properly stimulate the neurites, thus enhancing the formation of the NMJ, we performed different simulation tests of the electric field distribution, by varying the electrode type, size, position and applied voltage. Our results revealed that all the tested configurations did not induce an electric field dangerous for the cell vitality. Among these configurations, the one with cylindrical pin of 0.3 mm of radius, placed in the internal position of the neuronal chambers, allowed to obtain the highest electrical field in the zone comprising the neurites.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 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":"126081061","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/MeMeA52024.2021.9478702
F. Amitrano, A. Coccia, L. Donisi, G. Pagano, G. Cesarelli, G. D'Addio
Sock is a wearable e-textile sock for gait analysis. It is based on the acquisition and digital processing of the angular velocities of the lower limbs. In this paper we focus on the study of test-retest reliability of this system in measuring spatio-temporal gait parameters. The analysis was simultaneously conducted on data acquired by a multicamera system for gait analysis (SMART-DX 700 by BTS), in order to have reference values. A group of healthy subjects, equipped with both systems, performed four repeated walking tests along an 11 m walkway, consecutively and under constant conditions. The four tests were repeated at preferred, slow and fast self- selected walking speed. The Intraclass Correlation Coefficient (ICC) and Minimum Detectable Change (MDC) were evaluated to assess the repeatability of the measures. ICC values range from moderate to excellent for all gait parameters assessed by smart socks. The novel system presents test-retest reliability values comparable to, if not higher than, those shown by the gold standard. Finally, the results of gait reliability as a function of walking speed show excellent ICCs and very low MDCs for all parameters evaluated on trials at fast velocity, supporting the referenced hypothesis that faster movement is more consistent.
{"title":"Gait Analysis using Wearable E-Textile Sock: an Experimental Study of Test-Retest Reliability","authors":"F. Amitrano, A. Coccia, L. Donisi, G. Pagano, G. Cesarelli, G. D'Addio","doi":"10.1109/MeMeA52024.2021.9478702","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478702","url":null,"abstract":"Sock is a wearable e-textile sock for gait analysis. It is based on the acquisition and digital processing of the angular velocities of the lower limbs. In this paper we focus on the study of test-retest reliability of this system in measuring spatio-temporal gait parameters. The analysis was simultaneously conducted on data acquired by a multicamera system for gait analysis (SMART-DX 700 by BTS), in order to have reference values. A group of healthy subjects, equipped with both systems, performed four repeated walking tests along an 11 m walkway, consecutively and under constant conditions. The four tests were repeated at preferred, slow and fast self- selected walking speed. The Intraclass Correlation Coefficient (ICC) and Minimum Detectable Change (MDC) were evaluated to assess the repeatability of the measures. ICC values range from moderate to excellent for all gait parameters assessed by smart socks. The novel system presents test-retest reliability values comparable to, if not higher than, those shown by the gold standard. Finally, the results of gait reliability as a function of walking speed show excellent ICCs and very low MDCs for all parameters evaluated on trials at fast velocity, supporting the referenced hypothesis that faster movement is more consistent.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"25 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":"126030690","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/MeMeA52024.2021.9478741
Hedieh Hashem Olhosseiny, Mohammadsalar Mirzaloo, M. Bolic, H. Dajani, V. Groza, Masayoshi Yoshida
Atherosclerosis refers to the buildup of plaque on the artery walls. As the disease advances in its further stages, its burden could lead to stroke or heart attack. Atherosclerosis develops gradually, and mild stages of the condition are usually symptomless. Diagnosing patients in their early stages of the disease can facilitate timely clinical interventions enhancing patient’s quality of life by altering the course of the disease. The work presented in this paper is focused on classifying patients who are at high risk of Atherosclerosis using simple diagnosis tools available in every clinic. The final system is a prescreening tool providing the medical practitioners with recommendations regarding the disease. High risk patients can be referred to a cardiologist for further assessments. A dataset of 44 patients was collected including 17 low-risk and 27 high-risk patients. Two different approaches were taken, 1. using deep learning and time series data (ECG signals) 2. using traditional machine learning algorithms and tabular data. In the first approach, a Conv-GRU model was trained using ECG signals collected from patients. This method resulted in an average accuracy of 77% which was computed over 4 folds using cross validation. In the second approach, Stacking, an ensemble learning technique in which the final prediction is obtained by combining the prediction of different machine learning models trained on several attributes readily collected in the clinic, was used. An average accuracy of 81% was achieved using this method.
{"title":"Identifying High Risk of Atherosclerosis Using Deep Learning and Ensemble Learning","authors":"Hedieh Hashem Olhosseiny, Mohammadsalar Mirzaloo, M. Bolic, H. Dajani, V. Groza, Masayoshi Yoshida","doi":"10.1109/MeMeA52024.2021.9478741","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478741","url":null,"abstract":"Atherosclerosis refers to the buildup of plaque on the artery walls. As the disease advances in its further stages, its burden could lead to stroke or heart attack. Atherosclerosis develops gradually, and mild stages of the condition are usually symptomless. Diagnosing patients in their early stages of the disease can facilitate timely clinical interventions enhancing patient’s quality of life by altering the course of the disease. The work presented in this paper is focused on classifying patients who are at high risk of Atherosclerosis using simple diagnosis tools available in every clinic. The final system is a prescreening tool providing the medical practitioners with recommendations regarding the disease. High risk patients can be referred to a cardiologist for further assessments. A dataset of 44 patients was collected including 17 low-risk and 27 high-risk patients. Two different approaches were taken, 1. using deep learning and time series data (ECG signals) 2. using traditional machine learning algorithms and tabular data. In the first approach, a Conv-GRU model was trained using ECG signals collected from patients. This method resulted in an average accuracy of 77% which was computed over 4 folds using cross validation. In the second approach, Stacking, an ensemble learning technique in which the final prediction is obtained by combining the prediction of different machine learning models trained on several attributes readily collected in the clinic, was used. An average accuracy of 81% was achieved using this method.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"51 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":"127084031","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/MeMeA52024.2021.9478760
Yali Nie, M. Ferro, P. Sommella, M. Carratù, S. Cacciapuoti, G. D. Leo, J. Lundgren, G. Fabbrocini
Deep Convolution Neural Networks (CNN) enable advanced methods to predict the skin cancer classes through the automatic analysis of digital dermoscopic images. However, small datasets' availability often allows the models to be characterized by low prediction accuracy and poor generalization ability, which significantly influences clinical decisions. This paper proposes to use an original ensembling of multiple CNNs as feature extractors able to detect and measure skin lesions atypical criteria according to the well-known diagnostic method 7-Point Check List. The experimental results show that the Artificial Intelligence-based model can suitably manage the classification uncertainty of the single CNNs and finally distinguish melanomas from benignant nevi. Diagnostic performance is promising in terms of sensitivity and specificity towards a decision-supporting system used by a dermatologist with low experience during clinical practice.
{"title":"Ensembling CNNs for dermoscopic analysis of suspicious skin lesions","authors":"Yali Nie, M. Ferro, P. Sommella, M. Carratù, S. Cacciapuoti, G. D. Leo, J. Lundgren, G. Fabbrocini","doi":"10.1109/MeMeA52024.2021.9478760","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478760","url":null,"abstract":"Deep Convolution Neural Networks (CNN) enable advanced methods to predict the skin cancer classes through the automatic analysis of digital dermoscopic images. However, small datasets' availability often allows the models to be characterized by low prediction accuracy and poor generalization ability, which significantly influences clinical decisions. This paper proposes to use an original ensembling of multiple CNNs as feature extractors able to detect and measure skin lesions atypical criteria according to the well-known diagnostic method 7-Point Check List. The experimental results show that the Artificial Intelligence-based model can suitably manage the classification uncertainty of the single CNNs and finally distinguish melanomas from benignant nevi. Diagnostic performance is promising in terms of sensitivity and specificity towards a decision-supporting system used by a dermatologist with low experience during clinical practice.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"39 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":"133910953","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/MeMeA52024.2021.9478773
Yu-Chieh Chen, J. Tsan, Wen-Yen Lin
The accurate measurement of respiration rate in sleeping patients requires the patients to be in a comfortable state. Current measurement systems usually require patients to wear tights because the sensors must be close to the body to enable the acquisition of high-quality measurements. However, tights are uncomfortable when worn for a long period, especially during sleep. Moreover, current systems are marred by poor battery life, which is a major problem for overnight monitoring processes; existing battery designs cannot be integrated into smart clothing, which must be waterproof to protect electronic components during laundry.To solve these problems, this study developed a wireless power– supplied optical respiratory measurement module (wireless-ORM), which can be integrated with cotton clothing for the optical, noncontact measurement of respiratory rate. This module is powered wirelessly, which eliminates the need for a battery and allows for an indefinite power supply. The wireless-ORM can also be easily covered with a waterproof membrane for waterproofing. We fabricated and tested a prototype of the wireless-ORM measuring 197 × 20 × 3 mm3 in volume and 2.8 g in weight. The sensor was determined to function at distances up to 40 mm from the body, meaning that respiration rate could be measured even with thick winter clothes. The wireless-ORM could also receive power wirelessly up to 70 cm from a base station. Due to its small size, the wireless-ORM can be wrapped in plastic for waterproofing to enable its use in smart clothing.
{"title":"Wirelessly Powered Device for Optical Measurement of Respiration Rate","authors":"Yu-Chieh Chen, J. Tsan, Wen-Yen Lin","doi":"10.1109/MeMeA52024.2021.9478773","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478773","url":null,"abstract":"The accurate measurement of respiration rate in sleeping patients requires the patients to be in a comfortable state. Current measurement systems usually require patients to wear tights because the sensors must be close to the body to enable the acquisition of high-quality measurements. However, tights are uncomfortable when worn for a long period, especially during sleep. Moreover, current systems are marred by poor battery life, which is a major problem for overnight monitoring processes; existing battery designs cannot be integrated into smart clothing, which must be waterproof to protect electronic components during laundry.To solve these problems, this study developed a wireless power– supplied optical respiratory measurement module (wireless-ORM), which can be integrated with cotton clothing for the optical, noncontact measurement of respiratory rate. This module is powered wirelessly, which eliminates the need for a battery and allows for an indefinite power supply. The wireless-ORM can also be easily covered with a waterproof membrane for waterproofing. We fabricated and tested a prototype of the wireless-ORM measuring 197 × 20 × 3 mm3 in volume and 2.8 g in weight. The sensor was determined to function at distances up to 40 mm from the body, meaning that respiration rate could be measured even with thick winter clothes. The wireless-ORM could also receive power wirelessly up to 70 cm from a base station. Due to its small size, the wireless-ORM can be wrapped in plastic for waterproofing to enable its use in smart clothing.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"98 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":"126686051","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/MeMeA52024.2021.9478700
Nibras Abo Alzahab, M. Baldi, L. Scalise
Opposed to classic authentication protocols based on credentials, biometric-based authentication has recently emerged as a promising paradigm for achieving fast and secure authentication of users. Among the several families of biometric features, electroencephalogram (EEG)-based biometrics is considered as a promising approach due to its unique characteristics. Classification systems based on machine learning allow processing of large amounts of data and performing accurate attribution of each signal to the most relevant group, thus representing an invaluable tool for EEG-based biometrics. This paper provides an experimental evaluation of the performance achievable by EEG-based biometrics employing machine learning. We consider several groups of EEG signals and propose a suitable feature extraction criterion. Then, the extracted features are used along with neural network-based classification algorithms, K Nearest Neighbours (KNN), and eXtreme Gradient Boost (XGBoost) for attributing any EEG signal to a subject. A full feature set and a reduced feature sets are considered and tested on three public data sets. The feature selection criteria are based on a correlation map among features, ANOVA F-test, and logistic regression weights. The results show that the reduced feature sets achieves a significant reduction in computation time over the full feature set, while also providing some improvement in performance.
{"title":"Efficient feature selection for electroencephalogram-based authentication","authors":"Nibras Abo Alzahab, M. Baldi, L. Scalise","doi":"10.1109/MeMeA52024.2021.9478700","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478700","url":null,"abstract":"Opposed to classic authentication protocols based on credentials, biometric-based authentication has recently emerged as a promising paradigm for achieving fast and secure authentication of users. Among the several families of biometric features, electroencephalogram (EEG)-based biometrics is considered as a promising approach due to its unique characteristics. Classification systems based on machine learning allow processing of large amounts of data and performing accurate attribution of each signal to the most relevant group, thus representing an invaluable tool for EEG-based biometrics. This paper provides an experimental evaluation of the performance achievable by EEG-based biometrics employing machine learning. We consider several groups of EEG signals and propose a suitable feature extraction criterion. Then, the extracted features are used along with neural network-based classification algorithms, K Nearest Neighbours (KNN), and eXtreme Gradient Boost (XGBoost) for attributing any EEG signal to a subject. A full feature set and a reduced feature sets are considered and tested on three public data sets. The feature selection criteria are based on a correlation map among features, ANOVA F-test, and logistic regression weights. The results show that the reduced feature sets achieves a significant reduction in computation time over the full feature set, while also providing some improvement in performance.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"34 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":"125968397","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/MeMeA52024.2021.9478703
F. Vurchio, Gabriele Bocchetta, Giorgia Fiori, A. Scorza, N. Belfiore, S. Sciuto
This preliminary study concerns the dynamic characterization of a MEMS microgripper for biomedical applications. In particular, a prototype of microgripper, embedded with electrostatic comb-drive actuators, has been powered with a 10V sinusoidal input at different frequencies, 0.5 Hz, 1.0 Hz and 4.0 Hz. The response of the device has been recorded with a trinocular optical microscope, equipped with a digital camera and the recorded videos have been analysed with an in-house software implemented by the authors for the measurement of the comb-drive angular displacement, velocity and acceleration. The uncertainty analysis has been carried out to identify the uncertainty sources that characterize the measurements. Experimental data showed that the maximum angular displacement is (13.2 ± 0.2)•10-3 rad, (13.6 ± 0.2)•10-3 rad and (13.1 ± 0.3)•10-3 rad, the maximum angular velocity is (2.8 ± 0.2)•10-2 rad/s, (5.7 ± 0.4)•10-2 rad/s and (19.9 ± 1.5)•10-2 rad/s, and the angular acceleration is 0.178 ± 0.015 rad/s2, 0.72 ± 0.04 rad/s2 and 6.3 ± 0.7 rad/s2 for 0.5 Hz, 1.0 Hz and 4.0 Hz, respectively. The measurement results have been compared with the expected values from the theoretical model that describes the behaviour of the microgripper: the overall percentage error (PE) between the measured and the expected values at different frequencies is lower than 1%, 1% and 3% for the angular displacement, velocity and acceleration respectively.
{"title":"A preliminary study on the dynamic characterization of a MEMS microgripper for biomedical applications","authors":"F. Vurchio, Gabriele Bocchetta, Giorgia Fiori, A. Scorza, N. Belfiore, S. Sciuto","doi":"10.1109/MeMeA52024.2021.9478703","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478703","url":null,"abstract":"This preliminary study concerns the dynamic characterization of a MEMS microgripper for biomedical applications. In particular, a prototype of microgripper, embedded with electrostatic comb-drive actuators, has been powered with a 10V sinusoidal input at different frequencies, 0.5 Hz, 1.0 Hz and 4.0 Hz. The response of the device has been recorded with a trinocular optical microscope, equipped with a digital camera and the recorded videos have been analysed with an in-house software implemented by the authors for the measurement of the comb-drive angular displacement, velocity and acceleration. The uncertainty analysis has been carried out to identify the uncertainty sources that characterize the measurements. Experimental data showed that the maximum angular displacement is (13.2 ± 0.2)•10-3 rad, (13.6 ± 0.2)•10-3 rad and (13.1 ± 0.3)•10-3 rad, the maximum angular velocity is (2.8 ± 0.2)•10-2 rad/s, (5.7 ± 0.4)•10-2 rad/s and (19.9 ± 1.5)•10-2 rad/s, and the angular acceleration is 0.178 ± 0.015 rad/s2, 0.72 ± 0.04 rad/s2 and 6.3 ± 0.7 rad/s2 for 0.5 Hz, 1.0 Hz and 4.0 Hz, respectively. The measurement results have been compared with the expected values from the theoretical model that describes the behaviour of the microgripper: the overall percentage error (PE) between the measured and the expected values at different frequencies is lower than 1%, 1% and 3% for the angular displacement, velocity and acceleration respectively.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"60 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":"114180832","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/MeMeA52024.2021.9478736
Valentina Pasker, C. Huerta, Samuel Sainz, Darío Santos, F. Simini
Rehabilitation counteracts motor deficiencies in gait disorder of Parkinson's Disease (PD) patients. PARKIBIP is a wearable feedback device that aims to offer a continuous and personalized rehabilitation tool for such people. A survey and external study of PARKIBIP suggest design enhancements. Exploration of its industrial potential shows direct competitors, a first step to conclude that PARKIBIP is suitable for Technological Transfer to a company for commercial dissemination. PARKIBIP is both a home treatment helping device and a clinical data & feedback capture terminal for the electronic medical record. Being wearable technology, PARKIBIP stands out in the present global context as an affordable robotic element with feedback capability connected to the patient's mobile phone.
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