Pub Date : 2024-07-30DOI: 10.1109/LSENS.2024.3435965
Y. Hashimoto;S. Tada;Y. Nishida
Core body temperature (CBT) is one of the useful physiological indicators that are linked to physiological changes. Wearable sensors are expected to be developed to monitor CBT easily during activities. A single-heat-flux method is one of the noninvasive techniques that estimate CBT by measuring heat flow changes near the skin with temperature sensors. The method requires calibrating parameters for estimating CBT in advance by comparing them with reference values obtained through another method. The invasive measurement method is generally used to obtain the reference values, which poses a challenge in terms of measurement burden. Here, we propose a new calibration method that does not require acquiring reference values. This method identifies calibration parameters based on the temperature history after the measurement probe is attached. It has been numerically and experimentally confirmed that the estimation accuracy by the method is equivalent to that of a general-purpose electronic thermometer. This outcome decreases the measurement workload in CBT measurement using the single-heat-flux method and significantly enhances its usability.
{"title":"Reference-Free Calibration for Wearable Core Body Temperature Sensor Based on Single-Heat-Flux Method","authors":"Y. Hashimoto;S. Tada;Y. Nishida","doi":"10.1109/LSENS.2024.3435965","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3435965","url":null,"abstract":"Core body temperature (CBT) is one of the useful physiological indicators that are linked to physiological changes. Wearable sensors are expected to be developed to monitor CBT easily during activities. A single-heat-flux method is one of the noninvasive techniques that estimate CBT by measuring heat flow changes near the skin with temperature sensors. The method requires calibrating parameters for estimating CBT in advance by comparing them with reference values obtained through another method. The invasive measurement method is generally used to obtain the reference values, which poses a challenge in terms of measurement burden. Here, we propose a new calibration method that does not require acquiring reference values. This method identifies calibration parameters based on the temperature history after the measurement probe is attached. It has been numerically and experimentally confirmed that the estimation accuracy by the method is equivalent to that of a general-purpose electronic thermometer. This outcome decreases the measurement workload in CBT measurement using the single-heat-flux method and significantly enhances its usability.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/LSENS.2024.3435677
Matheus Minelli de Carvalho;L. H. Laurini;E. Atukpor;L. Naviner;Rodrigo Possamai Bastos
In this letter, we reveal the impact of increasing the sensor sampling frequency on the reliability of a typical edge processing system operating under the effects of 14-MeV neutrons and thermal neutrons. The results of two types of accelerated radiation tests indicate the rates of failures induced by soft errors caused by 14-MeV and thermal neutrons grow as a function of the sensor sampling frequency. The rate of failures caused by 14-MeV neutrons rose by factor of 2.2 by shifting the sensor sampling frequency from around 140 to 430 Hz. The results also suggest that the design and calibration of edge processing systems should consider the sensor sampling frequency as a parameter to finely tradeoff the computing speed of the system for improving the reliability in tolerating soft errors caused by neutrons.
{"title":"Impact of Scaling Up the Sensor Sampling Frequency on the Reliability of Edge Processing Systems in Tolerating Soft Errors Caused by Neutrons","authors":"Matheus Minelli de Carvalho;L. H. Laurini;E. Atukpor;L. Naviner;Rodrigo Possamai Bastos","doi":"10.1109/LSENS.2024.3435677","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3435677","url":null,"abstract":"In this letter, we reveal the impact of increasing the sensor sampling frequency on the reliability of a typical edge processing system operating under the effects of 14-MeV neutrons and thermal neutrons. The results of two types of accelerated radiation tests indicate the rates of failures induced by soft errors caused by 14-MeV and thermal neutrons grow as a function of the sensor sampling frequency. The rate of failures caused by 14-MeV neutrons rose by factor of 2.2 by shifting the sensor sampling frequency from around 140 to 430 Hz. The results also suggest that the design and calibration of edge processing systems should consider the sensor sampling frequency as a parameter to finely tradeoff the computing speed of the system for improving the reliability in tolerating soft errors caused by neutrons.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973464","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 : 2024-07-29DOI: 10.1109/LSENS.2024.3435009
Thivya Anbalagan;Malaya Kumar Nath
Atrial fibrillation (AF) is a life threatening cardiac abnormality having high prevalence and risk with increased rate of stroke and systemic embolism, if oral anticoagulation is not recommended. Later, this leads to morbidity and mortality. Detection of AF is challenging from the electrocardiogram (ECG) recordings, due to its complex characteristics. Manual observation of ECG is tedious, time consuming, and error prone. This manuscript proposed a novel approach for identifying AF in the presence of noise and other beats by using deep neural networks (DNN) on the 2-D patterns obtained by various time–frequency analysis methods, such as short time Fourier transform (STFT), Chirplet-transform, Stockwell-transform, and Poincare plot from 1-D preprocessed ECG recordings. The above discussed methods identify the variations due to AF in ECG. Initially, the patterns are used by the pretrained DNN models for classification. ResNet18 attained the highest accuracy of 90.56 $%$