Pub Date : 2024-08-20DOI: 10.1109/LSENS.2024.3446853
Sunil Mohan;Manish Singh Negi
This letter describes the development of a simple and novel optical fiber relative humidity (RH) sensor to be used for breath monitoring and voice recognition. The proposed sensor utilizes an intensity modulation phenomenon via evanescent wave (EW) absorption. The optical fiber sensor (OFS) employs a chemically synthesized nanostructured sensing film composed of multiwalled-carbon-nanotube-doped chitosan coated over a 5-cm length of a centrally decladded, straight, and uniform plastic cladding silica (PCS) fiber. A comprehensive experimental investigation is carried out to analyze the response characteristics of the proposed sensor. A linear response over the dynamic range of ∼70–97% RH with a sensitivity of 0.3041 dB/% RH is observed for the developed sensor. Furthermore, the resolution of the developed RH sensor is observed to be ±0.13% RH. An average response and recovery times of 100 and 150 ms are recorded during the humidification and dehumidification process. In addition, the proposed sensor demonstrates a high degree of repeatability, reversibility, and stability. Moreover, the developed sensor has the ability to detect RH fluctuations within exhaled air during both breathing and speaking.
{"title":"Carbon-Nanotube-Based Optical Fiber Sensor With Rapid Response for Human Breath Monitoring and Voiceprint Recognition","authors":"Sunil Mohan;Manish Singh Negi","doi":"10.1109/LSENS.2024.3446853","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3446853","url":null,"abstract":"This letter describes the development of a simple and novel optical fiber relative humidity (RH) sensor to be used for breath monitoring and voice recognition. The proposed sensor utilizes an intensity modulation phenomenon via evanescent wave (EW) absorption. The optical fiber sensor (OFS) employs a chemically synthesized nanostructured sensing film composed of multiwalled-carbon-nanotube-doped chitosan coated over a 5-cm length of a centrally decladded, straight, and uniform plastic cladding silica (PCS) fiber. A comprehensive experimental investigation is carried out to analyze the response characteristics of the proposed sensor. A linear response over the dynamic range of ∼70–97% RH with a sensitivity of 0.3041 dB/% RH is observed for the developed sensor. Furthermore, the resolution of the developed RH sensor is observed to be ±0.13% RH. An average response and recovery times of 100 and 150 ms are recorded during the humidification and dehumidification process. In addition, the proposed sensor demonstrates a high degree of repeatability, reversibility, and stability. Moreover, the developed sensor has the ability to detect RH fluctuations within exhaled air during both breathing and speaking.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090984","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 : 2024-08-20DOI: 10.1109/LSENS.2024.3446698
Sung-Won Kim;Sae-Byeok Kyung;Eun-Yul Lee;Ju-Won Kim
Flat belts are increasingly used in elevators, which offer faster stabilization and energy savings compared to wire ropes. How- ever, damage to flat belts during operation can lead to catastrophic accidents, such as rope failure and falls due to tensile loads. Therefore, there is a need for monitoring techniques to detect damage in advance and prevent accidents. Although extensive research has been conducted on the diagnosis of damage to wire ropes, studies on diagnosing damage to flat belts are lacking. In this letter, we propose a monitoring technique that applies the magnetic flux leakage (MFL) method to diagnose flat belt damage. MFL sensors utilizing permanent magnets were tailored for flat belts to measure the leakage flux. Six instances of artificial damage were created using samples of flat belts actually used in elevators, with damage induced at 30-cm intervals. Subsequently, MFL sensors were used to measure the leakage flux, confirming its occurrence in the damaged areas. Furthermore, as the degree of damage increased, the size of the leakage flux also increased. These findings confirm the potential of using MFL sensors for damage diagnosis through monitoring.
{"title":"Visualization Study for Enhancing the Efficiency of Local Damage Diagnosis on Flat Belts Based on MFL Technology","authors":"Sung-Won Kim;Sae-Byeok Kyung;Eun-Yul Lee;Ju-Won Kim","doi":"10.1109/LSENS.2024.3446698","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3446698","url":null,"abstract":"Flat belts are increasingly used in elevators, which offer faster stabilization and energy savings compared to wire ropes. How- ever, damage to flat belts during operation can lead to catastrophic accidents, such as rope failure and falls due to tensile loads. Therefore, there is a need for monitoring techniques to detect damage in advance and prevent accidents. Although extensive research has been conducted on the diagnosis of damage to wire ropes, studies on diagnosing damage to flat belts are lacking. In this letter, we propose a monitoring technique that applies the magnetic flux leakage (MFL) method to diagnose flat belt damage. MFL sensors utilizing permanent magnets were tailored for flat belts to measure the leakage flux. Six instances of artificial damage were created using samples of flat belts actually used in elevators, with damage induced at 30-cm intervals. Subsequently, MFL sensors were used to measure the leakage flux, confirming its occurrence in the damaged areas. Furthermore, as the degree of damage increased, the size of the leakage flux also increased. These findings confirm the potential of using MFL sensors for damage diagnosis through monitoring.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174022","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 : 2024-08-19DOI: 10.1109/LSENS.2024.3444810
Chirag Mehta;Pranav Sai Ananthoju;Swarubini PJ;Nagarajan Ganapathy
Elevated stress levels during pregnancy increase the risk of delivering a premature or low-birthweight infant. Recently, microecological momentary assessment (micro-EMA) has been explored extensively. However, capturing more distinct physiological responses to micro-EMA is still challenging. In this letter, we propose a methodology for micro-EMA-based stress detection using feature extraction and classifiers. For this, an online publicly available micro-EMA database (N=18) is considered. The ECG signals are preprocessed. Ten features are extracted and applied to the classifiers, namely, support vector machine, decision tree, gradient boosting (GradB), adaptive boosting, 1-D convolution network (DL), and, DL with fine-tuning (DLFT). Performance is evaluated using leave-one-subject-out cross-validation. The proposed approach is able to discriminate stress in pregnant mothers. Using DLFT, the approach yields an average classification F1 score, precision, and recall of 76.50 %, 72.40%, and 86.25%, respectively. Thus, the proposed approach could be extended for integrated monitoring systems, enabling real-time stress detection during pregnancy.
孕期压力水平升高会增加早产儿或低体重儿的出生风险。最近,人们对微生态瞬间评估(micro-EMA)进行了广泛的探索。然而,捕捉微生态瞬时评估中更多不同的生理反应仍具有挑战性。在这封信中,我们利用特征提取和分类器提出了一种基于微生态瞬间评估的压力检测方法。为此,我们考虑了一个在线公开微 EMA 数据库(N=18)。心电信号经过预处理。提取十个特征并应用于分类器,即支持向量机、决策树、梯度提升(GradB)、自适应提升、一维卷积网络(DL)和微调 DL(DLFT)。使用 "留一主体 "交叉验证法对性能进行了评估。所提出的方法能够分辨怀孕母亲的压力。使用 DLFT,该方法的平均分类 F1 得分、精确度和召回率分别为 76.50 %、72.40 % 和 86.25 %。因此,建议的方法可以扩展到综合监测系统中,实现孕期压力的实时检测。
{"title":"Automated Microstress Assessment During Pregnancy Using ECG Sensing and Optimized Deep Networks","authors":"Chirag Mehta;Pranav Sai Ananthoju;Swarubini PJ;Nagarajan Ganapathy","doi":"10.1109/LSENS.2024.3444810","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3444810","url":null,"abstract":"Elevated stress levels during pregnancy increase the risk of delivering a premature or low-birthweight infant. Recently, microecological momentary assessment (micro-EMA) has been explored extensively. However, capturing more distinct physiological responses to micro-EMA is still challenging. In this letter, we propose a methodology for micro-EMA-based stress detection using feature extraction and classifiers. For this, an online publicly available micro-EMA database (N=18) is considered. The ECG signals are preprocessed. Ten features are extracted and applied to the classifiers, namely, support vector machine, decision tree, gradient boosting (GradB), adaptive boosting, 1-D convolution network (DL), and, DL with fine-tuning (DLFT). Performance is evaluated using leave-one-subject-out cross-validation. The proposed approach is able to discriminate stress in pregnant mothers. Using DLFT, the approach yields an average classification F1 score, precision, and recall of 76.50 %, 72.40%, and 86.25%, respectively. Thus, the proposed approach could be extended for integrated monitoring systems, enabling real-time stress detection during pregnancy.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077613","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 : 2024-08-16DOI: 10.1109/LSENS.2024.3445153
Silvia Diaz;Miguel Ángel Armendáriz;Ignacio R. Matías
In this letter, we study the environmental sensing capabilities of a single-mode-multimode-single-mode (SMS) fiber in a simple low-cost configuration. SMS fibers exhibit sensitivity to temperature, humidity, refractive index, and strain, making them suitable for numerous applications in telecommunications, environmental monitoring, and more. Experimental results demonstrate that the sensor achieves a maximum temperature sensitivity of 4.53 nm/°C. In addition, SMS fibers can also work as humidity sensors by absorbing or releasing moisture, leading to variations in the refractive index. Monitoring these changes allows for precise humidity measurements, with a sensitivity of 0.1548 nm/%RH. Moreover, SMS fibers show a refractive index sensitivity of 39.65 nm/RIU and strain sensitivities as high as 1.062 nm/μϵ, indicating good performance.
{"title":"Single-Mode-Multimode-Single-Mode Fiber (SMS): Exploring Environmental Sensing Capabilities","authors":"Silvia Diaz;Miguel Ángel Armendáriz;Ignacio R. Matías","doi":"10.1109/LSENS.2024.3445153","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3445153","url":null,"abstract":"In this letter, we study the environmental sensing capabilities of a single-mode-multimode-single-mode (SMS) fiber in a simple low-cost configuration. SMS fibers exhibit sensitivity to temperature, humidity, refractive index, and strain, making them suitable for numerous applications in telecommunications, environmental monitoring, and more. Experimental results demonstrate that the sensor achieves a maximum temperature sensitivity of 4.53 nm/°C. In addition, SMS fibers can also work as humidity sensors by absorbing or releasing moisture, leading to variations in the refractive index. Monitoring these changes allows for precise humidity measurements, with a sensitivity of 0.1548 nm/%RH. Moreover, SMS fibers show a refractive index sensitivity of 39.65 nm/RIU and strain sensitivities as high as 1.062 nm/μϵ, indicating good performance.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142091055","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 : 2024-08-16DOI: 10.1109/LSENS.2024.3445162
C. Pichler;M. Neumayer;B. Schweighofer;C. Feilmayr;S. Schuster;H. Wegleiter
Monitoring the health of machinery in industrial environments is critical to prevent costly downtime and production disruptions. Acoustic measurements offer a promising alternative to traditional methods like vibration analysis due to their simpler instrumentation. However, accurately detecting fault sounds amidst high background noise remains a significant challenge. Machine learning approaches, for example, require extensive datasets encompassing normal and faulty operation to learn the machine's behavior. In this letter, we propose a different approach by focusing on knocking sounds, which are typical indicators of faults in industrial machinery. We describe these fault conditions using an appropriate signal model and use a general likelihood ratio test as a detector. As demonstrated in this letter, by accurately describing the fault pattern based on a small amount of fault data, very low false positive rates can be achieved, significantly reducing the effort required to collect extensive data sets for faulty machine operation.
{"title":"Knocking Sound Detection for Acoustic Condition Monitoring in Industrial Facilities","authors":"C. Pichler;M. Neumayer;B. Schweighofer;C. Feilmayr;S. Schuster;H. Wegleiter","doi":"10.1109/LSENS.2024.3445162","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3445162","url":null,"abstract":"Monitoring the health of machinery in industrial environments is critical to prevent costly downtime and production disruptions. Acoustic measurements offer a promising alternative to traditional methods like vibration analysis due to their simpler instrumentation. However, accurately detecting fault sounds amidst high background noise remains a significant challenge. Machine learning approaches, for example, require extensive datasets encompassing normal and faulty operation to learn the machine's behavior. In this letter, we propose a different approach by focusing on knocking sounds, which are typical indicators of faults in industrial machinery. We describe these fault conditions using an appropriate signal model and use a general likelihood ratio test as a detector. As demonstrated in this letter, by accurately describing the fault pattern based on a small amount of fault data, very low false positive rates can be achieved, significantly reducing the effort required to collect extensive data sets for faulty machine operation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313079","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}
In this letter, a temperature-sensing chip with a built-in photovoltaic (PV) energy harvesting circuit is proposed. The temperature-sensing circuit includes a bipolar-junction-transistor (BJT)-based sensing circuit, a gain stage, and a successive approximation register (SAR) analog-to-digital converter (ADC), while the energy harvesting circuit is a boost dc–dc converter with a perturbation-and-observation maximum power point tracking circuit. The main goal of this work is successful chip integration. To the best of our knowledge, this is the first chip that integrates a temperature sensor, an ADC, an energy harvesting circuit, a clock generator, and other related circuits into a single chip. While conventional temperature-sensing chips are typically powered by a stable power supply voltage (which may not be available in Internet of Things devices), the proposed chip is powered by the built-in boost converter, whose output voltage inevitably has ripples. Despite this, the performance of our temperature-sensing chip is excellent. In addition, the built-in clock generator can generate signals with a subhertz frequency, which is difficult to achieve with low-power requirements. The chip was fabricated using the TSMC 0.18-μm 1P6M mixed-signal process. The measured results indicate that the sensed temperature of the proposed chip ranges from –20 °C to 80 °C with 0.17 °C resolution. The error is within ±0.8 °C, and R