Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478604
Rachele Rossanigo, M. Caruso, F. Salis, S. Bertuletti, U. Croce, A. Cereatti
Stride length is often used to quantitatively evaluate human locomotion performance. Stride by stride estimation can be conveniently obtained from the signals recorded using miniaturized inertial sensors attached to the feet and appropriate algorithms for data fusion and integration. To reduce the detrimental drift effect, different algorithmic solutions can be implemented. However, the overall method accuracy is supposed to depend on the optimal selection of the parameters which are required to be set. This study aimed at evaluating the influence of the main parameters involved in well-established methods for stride length estimation. An optimization process was conducted to improve methods’ performance and preferable values for the considered parameters according to different walking speed ranges are suggested. A parametric solution is also proposed to target the methods on specific subjects’ gait characteristics. The stride length estimates were obtained from straight walking trials of five healthy volunteers and were compared with those obtained from a stereo-photogrammetric system. After parameters tuning, percentage errors for stride length were 1.9%, 2.5% and 2.6% for comfortable, slow, and fast walking conditions, respectively.
{"title":"An Optimal Procedure for Stride Length Estimation Using Foot-Mounted Magneto-Inertial Measurement Units","authors":"Rachele Rossanigo, M. Caruso, F. Salis, S. Bertuletti, U. Croce, A. Cereatti","doi":"10.1109/MeMeA52024.2021.9478604","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478604","url":null,"abstract":"Stride length is often used to quantitatively evaluate human locomotion performance. Stride by stride estimation can be conveniently obtained from the signals recorded using miniaturized inertial sensors attached to the feet and appropriate algorithms for data fusion and integration. To reduce the detrimental drift effect, different algorithmic solutions can be implemented. However, the overall method accuracy is supposed to depend on the optimal selection of the parameters which are required to be set. This study aimed at evaluating the influence of the main parameters involved in well-established methods for stride length estimation. An optimization process was conducted to improve methods’ performance and preferable values for the considered parameters according to different walking speed ranges are suggested. A parametric solution is also proposed to target the methods on specific subjects’ gait characteristics. The stride length estimates were obtained from straight walking trials of five healthy volunteers and were compared with those obtained from a stereo-photogrammetric system. After parameters tuning, percentage errors for stride length were 1.9%, 2.5% and 2.6% for comfortable, slow, and fast walking conditions, respectively.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"148 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":"115175751","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/MeMeA52024.2021.9478707
M. Romanato, A. Strazza, W. Piatkowska, F. Spolaor, S. Fioretti, D. Volpe, Z. Sawacha, F. Nardo
Surface electromyography (sEMG) is commonly adopted to characterize walking in patients affected by Parkinson’s disease (PD). Timing and morphology of sEMG signal are typically investigated, while poor information on frequency content is available. Thus, the present pilot study was designed to test the hypothesis that continuous wavelet transform (CWT) of sEMG signal is a suitable approach to assess muscle activity during PD-walking task, in both time and frequency domains. To this aim, sEMG signals from 4 leg muscles of 5 patients are acquired during walking and processed to assess CWT-scalogram function. Results show that CWT is able to provide time ranges of muscle-activation over the whole PD population, which matches with what reported in previous studies on PD. The novel contribution of this study consists in achieving a characterization of the frequency content of each one of regions detected in time domain. Although the frequency content does not exceed the typical frequency range between 5 Hz and 450 Hz, different mean frequency contents are observed among muscles and among different activations of the same muscle. In particular, a relevant variability of frequency content is observed for thigh muscles, showing differences up to 180 Hz between stance and swing values. In conclusion, present findings support the use of CWT scalogram for a reliable assessment of muscle activity in time-frequency domain, during walking of PD patients. Outcomes highlight a large inter and intra muscle variability of frequency range, opening a new field of investigation for future studies.
{"title":"Characterization of EMG time-frequency content during Parkinson walking: a pilot study","authors":"M. Romanato, A. Strazza, W. Piatkowska, F. Spolaor, S. Fioretti, D. Volpe, Z. Sawacha, F. Nardo","doi":"10.1109/MeMeA52024.2021.9478707","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478707","url":null,"abstract":"Surface electromyography (sEMG) is commonly adopted to characterize walking in patients affected by Parkinson’s disease (PD). Timing and morphology of sEMG signal are typically investigated, while poor information on frequency content is available. Thus, the present pilot study was designed to test the hypothesis that continuous wavelet transform (CWT) of sEMG signal is a suitable approach to assess muscle activity during PD-walking task, in both time and frequency domains. To this aim, sEMG signals from 4 leg muscles of 5 patients are acquired during walking and processed to assess CWT-scalogram function. Results show that CWT is able to provide time ranges of muscle-activation over the whole PD population, which matches with what reported in previous studies on PD. The novel contribution of this study consists in achieving a characterization of the frequency content of each one of regions detected in time domain. Although the frequency content does not exceed the typical frequency range between 5 Hz and 450 Hz, different mean frequency contents are observed among muscles and among different activations of the same muscle. In particular, a relevant variability of frequency content is observed for thigh muscles, showing differences up to 180 Hz between stance and swing values. In conclusion, present findings support the use of CWT scalogram for a reliable assessment of muscle activity in time-frequency domain, during walking of PD patients. Outcomes highlight a large inter and intra muscle variability of frequency range, opening a new field of investigation for future studies.","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":"115378309","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/MeMeA52024.2021.9478717
V. Sandulescu, S. Puscoci, Monica Petre, Minodora Dumitrache, Viorel Bota, Alexandru Gîrlea
The paper presents an application of mHealth – a mobile app for remote health monitoring, that facilitates using a Bluetooth enabled health measuring device and synchronizing health data to a health care services provider’s web portal. The mobile app uses a public API that allows its integration in a complex platform for home care providers, allowing health monitoring of large groups of patients, monitoring vital functions, including body temperature, respiratory rate and arterial blood oxygen saturation, relevant in monitoring COVID-19 patients.
{"title":"mHealth application for remote health monitoring useful during the COVID 19 pandemic","authors":"V. Sandulescu, S. Puscoci, Monica Petre, Minodora Dumitrache, Viorel Bota, Alexandru Gîrlea","doi":"10.1109/MeMeA52024.2021.9478717","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478717","url":null,"abstract":"The paper presents an application of mHealth – a mobile app for remote health monitoring, that facilitates using a Bluetooth enabled health measuring device and synchronizing health data to a health care services provider’s web portal. The mobile app uses a public API that allows its integration in a complex platform for home care providers, allowing health monitoring of large groups of patients, monitoring vital functions, including body temperature, respiratory rate and arterial blood oxygen saturation, relevant in monitoring COVID-19 patients.","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":"115564221","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/MeMeA52024.2021.9478704
N. S. Bonfiglio, Roberta Renati, Ludovica Patrone, D. Rollo, M. P. Penna
Impulsiveness and inhibitory control deficits represent one of the major difficulties in Attention Deficit disorders. It has been seen that these difficulties seem to be related to specific areas of the nervous system, such as the Prefrontal Area and the Inferior Frontal Gyrus. Several studies have shown how it is possible to improve inhibitory control and reduce impulsivity through the use of neurostimulation, in particular by single-session or multi-session protocols. Some of these researches have combined neurostimulation with tDCS with cognitive training, such as card or memory games, to improve the performance of some executive functions related to the frontal and prefrontal areas. The present work presents a treatment carried out on a 21-year-old subject with Attentive Disorder. The treatment consisted of the use of tDCS associated with cognitive training for 12 sessions. Cognitive batteries before starting the treatment and at the end of the treatment, as well as the trials on executive functions before each training session and at the end of the session, were administered. The results show an improvement in cognitive battery assessments before and after treatment. As regards the evaluations of executive function trials carried out for each session, however, the improvements are partial and related to some sessions. The results obtained in this work prove how the use of training, associated with neurostimulation, can represent an effective treatment for individuals with Attention Deficit. The possibility of using the protocol here proposed even remotely and without the assistance of an in presence operator, increases its potential and usefulness in care settings.
{"title":"The use of cognitive training with tDCS for the reduction of impulsiveness and improvement of executive functions: a case study","authors":"N. S. Bonfiglio, Roberta Renati, Ludovica Patrone, D. Rollo, M. P. Penna","doi":"10.1109/MeMeA52024.2021.9478704","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478704","url":null,"abstract":"Impulsiveness and inhibitory control deficits represent one of the major difficulties in Attention Deficit disorders. It has been seen that these difficulties seem to be related to specific areas of the nervous system, such as the Prefrontal Area and the Inferior Frontal Gyrus. Several studies have shown how it is possible to improve inhibitory control and reduce impulsivity through the use of neurostimulation, in particular by single-session or multi-session protocols. Some of these researches have combined neurostimulation with tDCS with cognitive training, such as card or memory games, to improve the performance of some executive functions related to the frontal and prefrontal areas. The present work presents a treatment carried out on a 21-year-old subject with Attentive Disorder. The treatment consisted of the use of tDCS associated with cognitive training for 12 sessions. Cognitive batteries before starting the treatment and at the end of the treatment, as well as the trials on executive functions before each training session and at the end of the session, were administered. The results show an improvement in cognitive battery assessments before and after treatment. As regards the evaluations of executive function trials carried out for each session, however, the improvements are partial and related to some sessions. The results obtained in this work prove how the use of training, associated with neurostimulation, can represent an effective treatment for individuals with Attention Deficit. The possibility of using the protocol here proposed even remotely and without the assistance of an in presence operator, increases its potential and usefulness in care settings.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"54 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":"116449968","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/memea52024.2021.9478759
{"title":"[MeMeA 2021 Front cover]","authors":"","doi":"10.1109/memea52024.2021.9478759","DOIUrl":"https://doi.org/10.1109/memea52024.2021.9478759","url":null,"abstract":"","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"6 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":"122409790","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/MeMeA52024.2021.9478685
Neslisah Gün, B. Karaböce, S. Yurdalan
Physiotherapy ultrasound is one of the most used electrophysical agents in the clinic for its widely therapeutic effects. To guarantee safe and effective treatment, physiotherapy devices must be capable of applying correct ultrasound radiation to the body (or part of the body) under the procedure. The effectiveness of the therapy depends on the ultrasound device's performance and calibration. For performance testing and calibration, tissue-mimicking phantoms are used. This study's objective is to characterize the acoustic parameters of muscle phantom used in the calibration of physiotherapy ultrasound, by following per under the metrology principles. Muscle phantom was prepared. Then, the phantom's sound speed, attenuation coefficient was measured with the pulse-echo method, density, and acoustic impedance calculations were performed. A detailed uncertainty study in measurements was also presented in the paper. The acoustic parameters of muscle phantom were measured as given; the speed of sound 1549.8 ± 3.89 m/s (expanded uncertainty, U=6.7), attenuation coefficient 1.14 ± 0.8 dB/cm MHz (expanded uncertainty, U=0.55). Its acoustic impedance was calculated as 1.632 MRayl; it's density as 1053.5 kg/ m³. Muscle phantom's acoustic properties were found similar to the muscle tissue. It can be used in testing the physiotherapy ultrasound device's performance and calibration as it is cheap, easy to make, and
{"title":"Characterization of Muscle Phantom Used in Calibration of Physiotherapy Ultrasound: Measurement of Acoustic Parameters of Phantom (2020)","authors":"Neslisah Gün, B. Karaböce, S. Yurdalan","doi":"10.1109/MeMeA52024.2021.9478685","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478685","url":null,"abstract":"Physiotherapy ultrasound is one of the most used electrophysical agents in the clinic for its widely therapeutic effects. To guarantee safe and effective treatment, physiotherapy devices must be capable of applying correct ultrasound radiation to the body (or part of the body) under the procedure. The effectiveness of the therapy depends on the ultrasound device's performance and calibration. For performance testing and calibration, tissue-mimicking phantoms are used. This study's objective is to characterize the acoustic parameters of muscle phantom used in the calibration of physiotherapy ultrasound, by following per under the metrology principles. Muscle phantom was prepared. Then, the phantom's sound speed, attenuation coefficient was measured with the pulse-echo method, density, and acoustic impedance calculations were performed. A detailed uncertainty study in measurements was also presented in the paper. The acoustic parameters of muscle phantom were measured as given; the speed of sound 1549.8 ± 3.89 m/s (expanded uncertainty, U=6.7), attenuation coefficient 1.14 ± 0.8 dB/cm MHz (expanded uncertainty, U=0.55). Its acoustic impedance was calculated as 1.632 MRayl; it's density as 1053.5 kg/ m³. Muscle phantom's acoustic properties were found similar to the muscle tissue. It can be used in testing the physiotherapy ultrasound device's performance and calibration as it is cheap, easy to make, and","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"43 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":"122681118","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/MeMeA52024.2021.9478676
Markus Schinle, Christina Erler, Timon Schneider, Joana Plewnia, Wilhelm Stork
Following the paradigm of precision medicine, the combination of health data and Machine Learning (ML) is promising to improve the quality of healthcare services e.g. by making diagnoses and therapeutic interventions as early and precise as possible. The implementation of this approach requires sufficient amounts of data with a high quality along the data life cycle. This goal seems recently achievable through the implementation of several national digital health strategies and the hope of a growing societal acceptance of digital health applications due to the implications of the COVID-19 pandemic. But, a collection of tools and methods is missing, which supports developers to use data as driving force of the development process. Due to the iterative nature of software application development, it allows the continuous improvement through the integration of collected digital data. We refer to this as a data-driven approach and identify steps to take and tools for its implementation. Associated challenges and opportunities of this translational approach are outlined on the example of a self-developed dementia screening application. Using our methodology, we compared multiple ML algorithms based on the data of an observational study (n=55) and achieved models with sensitivity up to 89% for unhealthy participants within this use case.
{"title":"Data-driven Development of Digital Health Applications on the Example of Dementia Screening","authors":"Markus Schinle, Christina Erler, Timon Schneider, Joana Plewnia, Wilhelm Stork","doi":"10.1109/MeMeA52024.2021.9478676","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478676","url":null,"abstract":"Following the paradigm of precision medicine, the combination of health data and Machine Learning (ML) is promising to improve the quality of healthcare services e.g. by making diagnoses and therapeutic interventions as early and precise as possible. The implementation of this approach requires sufficient amounts of data with a high quality along the data life cycle. This goal seems recently achievable through the implementation of several national digital health strategies and the hope of a growing societal acceptance of digital health applications due to the implications of the COVID-19 pandemic. But, a collection of tools and methods is missing, which supports developers to use data as driving force of the development process. Due to the iterative nature of software application development, it allows the continuous improvement through the integration of collected digital data. We refer to this as a data-driven approach and identify steps to take and tools for its implementation. Associated challenges and opportunities of this translational approach are outlined on the example of a self-developed dementia screening application. Using our methodology, we compared multiple ML algorithms based on the data of an observational study (n=55) and achieved models with sensitivity up to 89% for unhealthy participants within this use case.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"38 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":"125008804","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/MeMeA52024.2021.9478727
L. Donisi, P. Moretta, A. Coccia, F. Amitrano, A. Biancardi, G. D'Addio
Unilateral Spatial Neglect is a cognitive impairment of neuropsychological interest that is a consequence of stroke able to influence negatively the rehabilitation outcome of patients with stroke. The aim of the study is to explore the feasibility of machine learning to classify stroke patients with and without unilateral spatial neglect using clinical features. We performed the study using a machine learning approach by means the following tree-based algorithms: Decision Tree, Random Forest, Rotation Forest, AdaBoost of decision stumps and Gradient Boost tree using six clinical features both numerical and nominal: Montreal Cognitive Assessment, Functional Independence Measure scale, Barthel Index, aetiology, site of brain lesion and presence of hemiparesis at lower limbs. Tree-based Machine learning analysis achieved interesting results in terms of evaluation metrics scores; the best algorithm was Random Forest with an Accuracy, Sensitivity, Specificity, Precision and Area under the Receiver Operating Characteristic curve equal to 0.92, 0.83, 1.00, 1.00, 0.95 respectively. The study demonstrated the proposed combination of clinical features and algorithms represents a valuable approach to automatically classify stroke patients with and without Unilateral Spatial Neglect. The future investigations on enriched datasets will further confirm the potential application of this methodology as prognostic support to be chosen among those already implemented in the clinical field.
{"title":"Distinguishing Stroke patients with and without Unilateral Spatial Neglect by means of Clinical Features: a Tree-based Machine Learning Approach","authors":"L. Donisi, P. Moretta, A. Coccia, F. Amitrano, A. Biancardi, G. D'Addio","doi":"10.1109/MeMeA52024.2021.9478727","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478727","url":null,"abstract":"Unilateral Spatial Neglect is a cognitive impairment of neuropsychological interest that is a consequence of stroke able to influence negatively the rehabilitation outcome of patients with stroke. The aim of the study is to explore the feasibility of machine learning to classify stroke patients with and without unilateral spatial neglect using clinical features. We performed the study using a machine learning approach by means the following tree-based algorithms: Decision Tree, Random Forest, Rotation Forest, AdaBoost of decision stumps and Gradient Boost tree using six clinical features both numerical and nominal: Montreal Cognitive Assessment, Functional Independence Measure scale, Barthel Index, aetiology, site of brain lesion and presence of hemiparesis at lower limbs. Tree-based Machine learning analysis achieved interesting results in terms of evaluation metrics scores; the best algorithm was Random Forest with an Accuracy, Sensitivity, Specificity, Precision and Area under the Receiver Operating Characteristic curve equal to 0.92, 0.83, 1.00, 1.00, 0.95 respectively. The study demonstrated the proposed combination of clinical features and algorithms represents a valuable approach to automatically classify stroke patients with and without Unilateral Spatial Neglect. The future investigations on enriched datasets will further confirm the potential application of this methodology as prognostic support to be chosen among those already implemented in the clinical field.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"61 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":"127126537","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/MeMeA52024.2021.9478708
Giuliana Emmolo, Daryl Ma, Danilo Demarchi, P. Georgiou
The paradigm of Internet of Things (IoT) has revolutionised the field of human health monitoring. Recent research works outline an ever growing interest in the development of miniaturized fully functioning devices, where optimization strategies in terms of size, power consumption and data transmission capabilities represents the main requirements as well as the biggest challenges at the design stage. In this paper we provide an analysis into a data transmission method based on digital mixing for combining multiple inputs channels into a single output. We first demonstrate that the sources of the error generated in the output stream are the frequency ratio of the input signals and their relative phase shift. With the results from the simulations, we demonstrate that the error performed on the lower frequency information in the mixed signal has a trend which is exponentially decreasing with the input frequency ratio. Additionally, we prove that the relative phase shift of the input signals may significantly impact the error towards lower input frequency ratios. Afterwards, we analyze the system power consumption, and we demonstrate that the power trend is linear with the input frequency ratio. Lastly, we discuss the error performance versus power trade-off of the system, which is helpful for the design of the input frequency levels for a specific target application.
{"title":"Multiple Input, Single Output Frequency Mixing Communication Technique for Low Power Data Transmission","authors":"Giuliana Emmolo, Daryl Ma, Danilo Demarchi, P. Georgiou","doi":"10.1109/MeMeA52024.2021.9478708","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478708","url":null,"abstract":"The paradigm of Internet of Things (IoT) has revolutionised the field of human health monitoring. Recent research works outline an ever growing interest in the development of miniaturized fully functioning devices, where optimization strategies in terms of size, power consumption and data transmission capabilities represents the main requirements as well as the biggest challenges at the design stage. In this paper we provide an analysis into a data transmission method based on digital mixing for combining multiple inputs channels into a single output. We first demonstrate that the sources of the error generated in the output stream are the frequency ratio of the input signals and their relative phase shift. With the results from the simulations, we demonstrate that the error performed on the lower frequency information in the mixed signal has a trend which is exponentially decreasing with the input frequency ratio. Additionally, we prove that the relative phase shift of the input signals may significantly impact the error towards lower input frequency ratios. Afterwards, we analyze the system power consumption, and we demonstrate that the power trend is linear with the input frequency ratio. Lastly, we discuss the error performance versus power trade-off of the system, which is helpful for the design of the input frequency levels for a specific target application.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"59 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":"126228428","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/MeMeA52024.2021.9478722
Yihe Zhao, Libo Zhao, Gian Luca Barbruni, Zhikang Li, Zhuangde Jiang, S. Carrara
Capacitive micromachined ultrasonic transducers (CMUTs) operating at the series and parallel resonant frequencies, have shown a great potential in ultrasonic application and in biodetection. However, previous equivalent circuits rarely consider the fitting performance and measurement. This study proposes the establishment of the simplified equivalent circuits for the CMUTs-based device to analyze the electrical properties and the measurement sensitivity in liquid environment. We simulate a circular CMUT cell both in air and water through finite element method via COMSOL software, exploiting the multi-domain coupling method. We analyze the impedance behaviors of the CMUTs array with 100 cells under different direct current bias voltages (2 - 10V). Simultaneously, we successfully investigate the damping effects on the electrical characteristics such as impedance, phase, and quality factor. With the 4-element Butterworth-vanDyke model, two simplified equivalent lumped element models (LEMs) are demonstrated to fit the impedance curves of the CMUTs array around the series and parallel frequencies, respectively. Additionally, the sensitivity is evaluated using the simplified equivalent LEMs to explore the CMUTs array has a high normalized measurement sensitivity of 6.024 ppb/Hz at the parallel frequency.
{"title":"Equivalent Circuit Analysis of CMUTs-based Device for Measurement in Liquid Samples","authors":"Yihe Zhao, Libo Zhao, Gian Luca Barbruni, Zhikang Li, Zhuangde Jiang, S. Carrara","doi":"10.1109/MeMeA52024.2021.9478722","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478722","url":null,"abstract":"Capacitive micromachined ultrasonic transducers (CMUTs) operating at the series and parallel resonant frequencies, have shown a great potential in ultrasonic application and in biodetection. However, previous equivalent circuits rarely consider the fitting performance and measurement. This study proposes the establishment of the simplified equivalent circuits for the CMUTs-based device to analyze the electrical properties and the measurement sensitivity in liquid environment. We simulate a circular CMUT cell both in air and water through finite element method via COMSOL software, exploiting the multi-domain coupling method. We analyze the impedance behaviors of the CMUTs array with 100 cells under different direct current bias voltages (2 - 10V). Simultaneously, we successfully investigate the damping effects on the electrical characteristics such as impedance, phase, and quality factor. With the 4-element Butterworth-vanDyke model, two simplified equivalent lumped element models (LEMs) are demonstrated to fit the impedance curves of the CMUTs array around the series and parallel frequencies, respectively. Additionally, the sensitivity is evaluated using the simplified equivalent LEMs to explore the CMUTs array has a high normalized measurement sensitivity of 6.024 ppb/Hz at the parallel frequency.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"121 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":"116769784","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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