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 2 representing linearity reaches 0.99988.
{"title":"Photovoltaic-Energy-Powered Temperature-Sensing Chip With Digital Output and Built-in Energy Harvesting Circuit","authors":"Yen-Ju Lin;Jian-Zhou Yan;Kai-Min Chang;Chia-Ling Wei","doi":"10.1109/LSENS.2024.3443274","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3443274","url":null,"abstract":"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 \u0000<italic>R</i>\u0000<sup>2</sup>\u0000 representing linearity reaches 0.99988.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084486","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 : 2024-08-14DOI: 10.1109/LSENS.2024.3443461
Melanys Benitez;Pablo Zubiate;Abián B. Socorro;Ignacio R. Matías
Celiac disease (CD) is a chronic autoimmune disorder triggered by gluten consumption, which affects between 0.5% and 1% of the global population. Current diagnostic methods still require invasive pro-cedures, such as intestinal biopsy. Lossy mode resonance (LMR)-based sensors hold great potential for the development of reliable and user-friendly devices for diagnosing this condition. In this letter, an LMR planar microfluidic system is used to perform the label-free detection of different concentrations of antigliadin antibodies, one of the biomarkers of celiac disease, achieving a limit of detection of 1 µg/mL. The speci- ficity of the sensor to the target analyte is also proved, and the validation of the biofunctionalization process is comple- mented with an atomic force microscope analysis.
{"title":"Revealing the Capability of an LMR Microfluidic Biosensor for Celiac Disease Diagnosis via Label-Free Detection of Antigliadin Antibodies","authors":"Melanys Benitez;Pablo Zubiate;Abián B. Socorro;Ignacio R. Matías","doi":"10.1109/LSENS.2024.3443461","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3443461","url":null,"abstract":"Celiac disease (CD) is a chronic autoimmune disorder triggered by gluten consumption, which affects between 0.5% and 1% of the global population. Current diagnostic methods still require invasive pro-cedures, such as intestinal biopsy. Lossy mode resonance (LMR)-based sensors hold great potential for the development of reliable and user-friendly devices for diagnosing this condition. In this letter, an LMR planar microfluidic system is used to perform the label-free detection of different concentrations of antigliadin antibodies, one of the biomarkers of celiac disease, achieving a limit of detection of 1 µg/mL. The speci- ficity of the sensor to the target analyte is also proved, and the validation of the biofunctionalization process is comple- mented with an atomic force microscope analysis.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368496","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 : 2024-08-14DOI: 10.1109/LSENS.2024.3443526
Jonas Sandelin;Ismail Elnaggar;Olli Lahdenoja;Matti Kaisti;Tero Koivisto
Acquiring labeled data for machine learning algorithms in healthcare is expensive due to the laborious expert annotation and privacy concerns. This challenge is further complicated in the case of mechanocardiogram (MCG) data, which are characterized by high interpersonal and intrapersonal complexity, compounded further by sensor variability. In this letter, we introduce an innovative method for generating synthetic MCG signals to address the scarcity of labeled data necessary for training machine learning models in healthcare. Our approach involves generating RR-intervals, adding wavelets, and incorporating noise to create realistic synthetic MCG signals. These synthetic signals were used to train a convolutional neural network for peak detection in real MCG data. Our key contributions include developing a detailed methodology for realistic synthetic MCG signal generation, reducing the mean absolute error in peak detection by 4.88 beats per minute using synthetic data, enhancing the training of machine learning models, creating a new peak detection method, and addressing data scarcity in biomedical signal processing. These contributions emphasize the methodological innovations and the significance of our results, underscoring the potential impact of synthetic data in improving healthcare diagnostics.
在医疗保健领域，为机器学习算法获取标注数据的成本很高，这是因为专家注释费力且存在隐私问题。机械心动图（MCG）数据具有高度的人际和人内复杂性，而传感器的可变性又进一步加剧了这一挑战的复杂性。在这封信中，我们介绍了一种生成合成 MCG 信号的创新方法，以解决医疗保健领域训练机器学习模型所需的标记数据稀缺的问题。我们的方法包括生成 RR 间隔、添加小波并加入噪声，以创建逼真的合成 MCG 信号。这些合成信号用于训练卷积神经网络，以检测真实 MCG 数据中的峰值。我们的主要贡献包括：开发了生成真实合成 MCG 信号的详细方法；使用合成数据将峰值检测的平均绝对误差降低了 4.88 次/分钟；增强了机器学习模型的训练；创建了一种新的峰值检测方法；以及解决了生物医学信号处理中的数据稀缺问题。这些贡献强调了我们的方法创新和成果意义，突出了合成数据在改善医疗诊断方面的潜在影响。
{"title":"Generating Synthetic Mechanocardiograms for Machine Learning-Based Peak Detection","authors":"Jonas Sandelin;Ismail Elnaggar;Olli Lahdenoja;Matti Kaisti;Tero Koivisto","doi":"10.1109/LSENS.2024.3443526","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3443526","url":null,"abstract":"Acquiring labeled data for machine learning algorithms in healthcare is expensive due to the laborious expert annotation and privacy concerns. This challenge is further complicated in the case of mechanocardiogram (MCG) data, which are characterized by high interpersonal and intrapersonal complexity, compounded further by sensor variability. In this letter, we introduce an innovative method for generating synthetic MCG signals to address the scarcity of labeled data necessary for training machine learning models in healthcare. Our approach involves generating RR-intervals, adding wavelets, and incorporating noise to create realistic synthetic MCG signals. These synthetic signals were used to train a convolutional neural network for peak detection in real MCG data. Our key contributions include developing a detailed methodology for realistic synthetic MCG signal generation, reducing the mean absolute error in peak detection by 4.88 beats per minute using synthetic data, enhancing the training of machine learning models, creating a new peak detection method, and addressing data scarcity in biomedical signal processing. These contributions emphasize the methodological innovations and the significance of our results, underscoring the potential impact of synthetic data in improving healthcare diagnostics.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328418","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 : 2024-08-13DOI: 10.1109/LSENS.2024.3442830
Chandan;Mainak Chakraborty;Sahil Anchal;Bodhibrata Mukhopadhyay;Subrat Kar
We introduce “GajGamini:” a novel method for detecting elephant movement by analyzing ground vibrations recorded using seismic sensors. This method is based on the principle that ground vibrations from elephants are distinct from those caused by humans and background noise. In this letter, we address two main challenges. First, there was a lack of studies with extensive data on vibrations from Indian elephants and humans. To address this, we recorded 3 h of elephant movements and 2 h of human movements using seismic sensors. Second, there was a need for a dedicated architecture for the real-time classification of seismic vibrations from elephants, humans, and background noise. To overcome this, we propose a convolutional neural network (CNN)–based model named “GajGamini” that achieves a prediction accuracy of ${sim}98.03%$ with only 3 s of computational runtime for every 10 s of recorded data. GajGamini represents a significant advancement in wildlife monitoring, particularly for elephant conservation. It offers a noninvasive way to track elephant movements, enhancing the effectiveness of wildlife management strategies.
{"title":"GajGamini: Mitigating Man–Animal Conflict by Detecting Moving Elephants Using Ground Vibration-Based Seismic Sensor","authors":"Chandan;Mainak Chakraborty;Sahil Anchal;Bodhibrata Mukhopadhyay;Subrat Kar","doi":"10.1109/LSENS.2024.3442830","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3442830","url":null,"abstract":"We introduce “GajGamini:” a novel method for detecting elephant movement by analyzing ground vibrations recorded using seismic sensors. This method is based on the principle that ground vibrations from elephants are distinct from those caused by humans and background noise. In this letter, we address two main challenges. First, there was a lack of studies with extensive data on vibrations from Indian elephants and humans. To address this, we recorded 3 h of elephant movements and 2 h of human movements using seismic sensors. Second, there was a need for a dedicated architecture for the real-time classification of seismic vibrations from elephants, humans, and background noise. To overcome this, we propose a convolutional neural network (CNN)–based model named “GajGamini” that achieves a prediction accuracy of \u0000<inline-formula><tex-math>${sim}98.03%$</tex-math></inline-formula>\u0000 with only 3 s of computational runtime for every 10 s of recorded data. GajGamini represents a significant advancement in wildlife monitoring, particularly for elephant conservation. It offers a noninvasive way to track elephant movements, enhancing the effectiveness of wildlife management strategies.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090983","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 : 2024-08-13DOI: 10.1109/LSENS.2024.3443072
H Pallab Jyoti Dutta;M. K. Bhuyan
Hand keypoint localization is used extensively in human–computer interaction, but accurate localization is challenging due to closeness between the fingers and the keypoints, occlusion, varied hand poses, complex backgrounds, and extreme lighting conditions. Despite much research, challenges persist. Therefore, we propose an encoder–decoder architecture aided by a novel attention module to precisely localize hand keypoints. The attention module captures keypoint-relevant features at two different scales that encompass local and global characteristics. Further, the loss function teaches the model to remove spurious detected keypoints in the initial learning phase. The proposed architecture outputs precise keypoint locations, as indicated by the qualitative and quantitative results. Evaluation of two benchmark RGB image datasets, comprising all the challenges encountered in keypoint localization, resulted in endpoint errors as low as 2.78 and 1.85 pixels and 98.50% and 99.77% correct keypoints, respectively. This shows the proposed model's effectiveness and ability to overcome challenges.
{"title":"Attention-Based 2-D Hand Keypoints Localization","authors":"H Pallab Jyoti Dutta;M. K. Bhuyan","doi":"10.1109/LSENS.2024.3443072","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3443072","url":null,"abstract":"Hand keypoint localization is used extensively in human–computer interaction, but accurate localization is challenging due to closeness between the fingers and the keypoints, occlusion, varied hand poses, complex backgrounds, and extreme lighting conditions. Despite much research, challenges persist. Therefore, we propose an encoder–decoder architecture aided by a novel attention module to precisely localize hand keypoints. The attention module captures keypoint-relevant features at two different scales that encompass local and global characteristics. Further, the loss function teaches the model to remove spurious detected keypoints in the initial learning phase. The proposed architecture outputs precise keypoint locations, as indicated by the qualitative and quantitative results. Evaluation of two benchmark RGB image datasets, comprising all the challenges encountered in keypoint localization, resulted in endpoint errors as low as 2.78 and 1.85 pixels and 98.50% and 99.77% correct keypoints, respectively. This shows the proposed model's effectiveness and ability to overcome challenges.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050474","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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