This letter presents an on-chip scale crack sensor for solder joints using radio frequency signals, essential for enhancing the reliability of electronic packages. The sensor design includes a Class F power amplifier and an envelope detector, based on an equivalent circuit model of cracked solder joints. Circuit simulations reveal that as a crack initiate, a resonant dip in the S-parameter pattern appears, with the resonant frequency decreasing as the crack propagates. Leveraging the resonant dip as a prognostic factor, the sensor can accurately characterize cracks in solder joints with easy-to-handle dc output. The sensor, which provides a maximum crack length sensitivity of 0.05 GHz/ $upmu$ m, is highly sensitive and can be fabricated on-chip.
这封信介绍了一种使用射频信号的片上焊点裂纹传感器，它对提高电子封装的可靠性至关重要。该传感器的设计包括一个 F 类功率放大器和一个包络探测器，均基于裂纹焊点的等效电路模型。电路仿真显示，随着裂纹的产生，S 参数模式中会出现共振凹陷，共振频率会随着裂纹的扩展而降低。利用共振凹陷作为预报因素，传感器可以准确地描述焊点裂纹的特征，并提供易于处理的直流输出。该传感器的最大裂纹长度灵敏度为 0.05 GHz/$upmu$m，灵敏度高，可在芯片上制造。
{"title":"On-Chip Sensor for Monitoring Crack Propagation in Solder Joints Using RF Signals: Electrical Modeling and Circuit Design","authors":"Tae Yeob Kang;Yunah Her;Byeongcheol Choe;Gwang-hyeon Jeong","doi":"10.1109/LSENS.2024.3459031","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3459031","url":null,"abstract":"This letter presents an on-chip scale crack sensor for solder joints using radio frequency signals, essential for enhancing the reliability of electronic packages. The sensor design includes a Class F power amplifier and an envelope detector, based on an equivalent circuit model of cracked solder joints. Circuit simulations reveal that as a crack initiate, a resonant dip in the S-parameter pattern appears, with the resonant frequency decreasing as the crack propagates. Leveraging the resonant dip as a prognostic factor, the sensor can accurately characterize cracks in solder joints with easy-to-handle dc output. The sensor, which provides a maximum crack length sensitivity of 0.05 GHz/\u0000<inline-formula><tex-math>$upmu$</tex-math></inline-formula>\u0000m, is highly sensitive and can be fabricated on-chip.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359759","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}
Electroencephalography (EEG) channel selection is crucial for improving the accuracy and efficiency of EEG-based brain-computer interfaces (BCI) and cognitive state monitoring systems. This research identifies the most informative EEG channels that provide maximum discriminative power for specific tasks or applications. However, the availability of multiple electrodes can lead to data redundancy and increased computational complexity. In addition, selecting suboptimal channels may result in poor signal quality and reduced classification accuracy. A method called $k$ -adaptEEGCS is proposed in this study to address these challenges. $k$ -adaptEEGCS utilizes a similarity-metric-based approach to measure the similarity of EEG channels within each cluster and identify the best EEG channels using an adaptive threshold. The results show that $k$ -adaptEEGCS improves classification accuracy and reduces channel selection time in specific EEG groups compared to using all EEG channels. Furthermore, the efficacy and superiority of $k$ -adaptEEGCS are demonstrated through an analysis of BCI competition datasets; the average accuracy and channel reduction rate achieved is 93.09% and 67%.
{"title":"$k$-AdaptEEGCS: Adaptive Threshold Based Automatic EEG Channel Selection","authors":"Abdullah;Ibrahima Faye;Mohammad Tanveer;Anudeep Vurity","doi":"10.1109/LSENS.2024.3458996","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3458996","url":null,"abstract":"Electroencephalography (EEG) channel selection is crucial for improving the accuracy and efficiency of EEG-based brain-computer interfaces (BCI) and cognitive state monitoring systems. This research identifies the most informative EEG channels that provide maximum discriminative power for specific tasks or applications. However, the availability of multiple electrodes can lead to data redundancy and increased computational complexity. In addition, selecting suboptimal channels may result in poor signal quality and reduced classification accuracy. A method called \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-adaptEEGCS is proposed in this study to address these challenges. \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-adaptEEGCS utilizes a similarity-metric-based approach to measure the similarity of EEG channels within each cluster and identify the best EEG channels using an adaptive threshold. The results show that \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-adaptEEGCS improves classification accuracy and reduces channel selection time in specific EEG groups compared to using all EEG channels. Furthermore, the efficacy and superiority of \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-adaptEEGCS are demonstrated through an analysis of BCI competition datasets; the average accuracy and channel reduction rate achieved is 93.09% and 67%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274996","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}
Falls in people with Parkinson's disease (PwPD) under- score the need for precise sensing tools to robustly assess gait and deliver tailored rehabilitation. Using wearable inertial measurement units (IMUs) offers a practical alternative to assess gait and intervene in any location. This study develops a robust and innovative smartphone application/app that uses embedded IMU for real-time gait sensing to facilitate personalized cueing for targeted rehabilitation to reduce falls. Here, older adults had their CuePD -based gait validated against a reference standard and were then exposed to different but personalized cueing modalities to target a 10.0% increase in cadence. CuePD increased cadence by 8.3% and showed robust agreement with the reference before and after cueing as evidenced by strong Pearson correlation coefficients (≥0.843) and intraclass correlation coefficients (≥0.845) across clinically relevant temporal gait characteristics (e.g., step time). Gait sensing via a smartphone is robust and CuePD indicates the feasibility of a scalable and personalized approach for targeted gait rehabilitation. Future research will extend to PwPD.
{"title":"CuePD: An IoT Approach for Enhancing Gait Rehabilitation in Older Adults Through Personalized Music Cueing","authors":"Conor Wall;Fraser Young;Peter McMeekin;Victoria Hetherington;Richard Walker;Rosie Morris;Gill Barry;Yunus Celik;Alan Godfrey","doi":"10.1109/LSENS.2024.3456855","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456855","url":null,"abstract":"Falls in people with Parkinson's disease (PwPD) under- score the need for precise sensing tools to robustly assess gait and deliver tailored rehabilitation. Using wearable inertial measurement units (IMUs) offers a practical alternative to assess gait and intervene in any location. This study develops a robust and innovative smartphone application/app that uses embedded IMU for real-time gait sensing to facilitate personalized cueing for targeted rehabilitation to reduce falls. Here, older adults had their \u0000<italic>CuePD</i>\u0000-based gait validated against a reference standard and were then exposed to different but personalized cueing modalities to target a 10.0% increase in cadence. \u0000<italic>CuePD</i>\u0000 increased cadence by 8.3% and showed robust agreement with the reference before and after cueing as evidenced by strong Pearson correlation coefficients (≥0.843) and intraclass correlation coefficients (≥0.845) across clinically relevant temporal gait characteristics (e.g., step time). Gait sensing via a smartphone is robust and \u0000<italic>CuePD</i>\u0000 indicates the feasibility of a scalable and personalized approach for targeted gait rehabilitation. Future research will extend to PwPD.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313104","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}
This letter presents a bioimpedance (Bio-Z) sensing interface IC with a current matching and common-mode suppressing (CMCS) loop for long-term health monitoring applications. The CMCS loop eliminates the mismatch between the source and sink stimulation currents of the current generator (CG), while reducing the area of the CG by half. Furthermore, the CMCS loop reuses the common-mode feedback loop of the Bio-Z instrumentation amplifier to substantially enhance the common-mode rejection ratio (CMRR) of the interface with reduced power consumption and area overhead. The proposed Bio-Z interface is designed in 130-nm CMOS technology. By employing the CMCS loop, it achieves a CMRR of 127 dB, exhibits a 3.84-mΩ/√Hz input-referred impedance noise, occupies an area of 0.07 mm 2 , and consumes a power of 10.4 µW for the readout front end and 12.9–122.7 µW for the CG.
{"title":"A Bioimpedance Sensing Interface IC With Current Matching and Common-Mode Suppressing Loop for Wearable Health Monitoring","authors":"Risheng Su;Longbin Zhu;Wenjie Wang;Yang Zhou;Jianan Zheng;Zhengtao Zhu;Fan Luo;Siyuan Xie;Jihong Li;Zhijun Zhou;Fanyi Meng;Keping Wang","doi":"10.1109/LSENS.2024.3457614","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3457614","url":null,"abstract":"This letter presents a bioimpedance (Bio-Z) sensing interface IC with a current matching and common-mode suppressing (CMCS) loop for long-term health monitoring applications. The CMCS loop eliminates the mismatch between the source and sink stimulation currents of the current generator (CG), while reducing the area of the CG by half. Furthermore, the CMCS loop reuses the common-mode feedback loop of the Bio-Z instrumentation amplifier to substantially enhance the common-mode rejection ratio (CMRR) of the interface with reduced power consumption and area overhead. The proposed Bio-Z interface is designed in 130-nm CMOS technology. By employing the CMCS loop, it achieves a CMRR of 127 dB, exhibits a 3.84-mΩ/√Hz input-referred impedance noise, occupies an area of 0.07 mm\u0000<sup>2</sup>\u0000, and consumes a power of 10.4 µW for the readout front end and 12.9–122.7 µW for the CG.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324235","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}
Defects in the internal structure of cement-based materials can be identified through the electrical resistivity of cement-based materials. This letter focuses on detecting defects using an electrode array with optimized distribution. The defects were imaged and analyzed by demonstrating the effectiveness of this detection method. Based on the results of resistivity measurements with different main electrode spacing and auxiliary electrode positions, the optimal electrode distribution was determined to be 150 mm for the main electrode spacing and 5 mm for the auxiliary electrode from the edge of the test block. Defects were successfully detected using the electrode array with optimized distribution; the imaging results can accurately show the location of the defect and can initially reflect the shape of the defect. Therefore, this optimized electrode array makes it possible to detect defects through resistivity measurements of the cement-based materials, which provides us a new solution to the application of the optimized electrode array for the detection of cement paste or concrete defects in the practical engineering project.
{"title":"Optimization of Electrode Distribution for Detecting Defects in Electrical Resistivity of Embedded Electrode Arrays in Cement-Based Materials","authors":"Songmao Yang;Lin Chi;Qianrui Zhang;Qi Liang;Shuang Lu;Yuhao Zhang","doi":"10.1109/LSENS.2024.3456905","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456905","url":null,"abstract":"Defects in the internal structure of cement-based materials can be identified through the electrical resistivity of cement-based materials. This letter focuses on detecting defects using an electrode array with optimized distribution. The defects were imaged and analyzed by demonstrating the effectiveness of this detection method. Based on the results of resistivity measurements with different main electrode spacing and auxiliary electrode positions, the optimal electrode distribution was determined to be 150 mm for the main electrode spacing and 5 mm for the auxiliary electrode from the edge of the test block. Defects were successfully detected using the electrode array with optimized distribution; the imaging results can accurately show the location of the defect and can initially reflect the shape of the defect. Therefore, this optimized electrode array makes it possible to detect defects through resistivity measurements of the cement-based materials, which provides us a new solution to the application of the optimized electrode array for the detection of cement paste or concrete defects in the practical engineering project.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316294","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 : 2024-09-10DOI: 10.1109/LSENS.2024.3457169
Yi Zhou;Miguel López-Benítez;Limin Yu;Yutao Yue
Radar-based human activity recognition (HAR) is popular because of its privacy and contactless sensing capabilities. However, a major challenge in this area is the lack of large and diverse datasets. In response, we present a novel framework that uses generative models to transform textual descriptions into motion data, thereby simulating radar signals. This approach significantly enriches the realism and diversity of the dataset, especially for infrequent but critical activities, such as falls and abnormal walking. Textual descriptions capture the semantic complexity of human actions, thereby improving intraclass diversity. Our framework scales the data generation process by using a lightweight physics-based simulator and improves diversity by controlling gait variation, multiviewpoint adaptation, and background noise modeling. The experiments show that data diversity is a critical factor for fair model comparisons, and that the simulated data can effectively improve performance through sim-to-real transfer learning.
{"title":"Text2Doppler: Generating Radar Micro–Doppler Signatures for Human Activity Recognition via Textual Descriptions","authors":"Yi Zhou;Miguel López-Benítez;Limin Yu;Yutao Yue","doi":"10.1109/LSENS.2024.3457169","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3457169","url":null,"abstract":"Radar-based human activity recognition (HAR) is popular because of its privacy and contactless sensing capabilities. However, a major challenge in this area is the lack of large and diverse datasets. In response, we present a novel framework that uses generative models to transform textual descriptions into motion data, thereby simulating radar signals. This approach significantly enriches the realism and diversity of the dataset, especially for infrequent but critical activities, such as falls and abnormal walking. Textual descriptions capture the semantic complexity of human actions, thereby improving intraclass diversity. Our framework scales the data generation process by using a lightweight physics-based simulator and improves diversity by controlling gait variation, multiviewpoint adaptation, and background noise modeling. The experiments show that data diversity is a critical factor for fair model comparisons, and that the simulated data can effectively improve performance through sim-to-real transfer learning.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316346","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}
Conventional millimeter wave (mmwave) point cloud generation technology suffers from information loss due to sparse scattering points on targets. Existing works generate and fuse radar data to enhance the point cloud, but they either demand datasets or consume extra resources. This letter proposes a virtual multiview fusion system for mmwave point cloud generation to attain complete target characteristics with the least resources. In our system, we set a single radar for sensing and regard radar signals relying on walls as virtual detection from multiple views. Then, we fuse target features detected from virtual views to the direct path detection to densify the point cloud. Instead of mitigation, multipath components are reserved and employed as supplements. It contains new characteristics from different perspectives, effectively compensating for the specular reflection loss without additional detection. Experiments are performed to validate the effectiveness of the proposed system in generating a dense radar point cloud.
{"title":"Virtual Multiview Fusion for Millimeter Wave Radar Point Cloud Generation","authors":"Xiaotong Lu;Guanghua Liu;You Xu;Chao Xie;Lixia Xiao;Tao Jiang","doi":"10.1109/LSENS.2024.3456840","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456840","url":null,"abstract":"Conventional millimeter wave (mmwave) point cloud generation technology suffers from information loss due to sparse scattering points on targets. Existing works generate and fuse radar data to enhance the point cloud, but they either demand datasets or consume extra resources. This letter proposes a virtual multiview fusion system for mmwave point cloud generation to attain complete target characteristics with the least resources. In our system, we set a single radar for sensing and regard radar signals relying on walls as virtual detection from multiple views. Then, we fuse target features detected from virtual views to the direct path detection to densify the point cloud. Instead of mitigation, multipath components are reserved and employed as supplements. It contains new characteristics from different perspectives, effectively compensating for the specular reflection loss without additional detection. Experiments are performed to validate the effectiveness of the proposed system in generating a dense radar point cloud.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313147","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 : 2024-09-10DOI: 10.1109/LSENS.2024.3457243
Vivek Kumar Singh;Ram Bilas Pachori
In this letter, we propose a new framework for Alzheimer's disease (AD) detection using electroencephalogram (EEG) signals. The EEG signals are decomposed into a set of elementary components using improved multichannel eigenvalue decomposition of Hankel matrix (MCh-EVDHM) technique. A rhythm separation method is proposed based on improved MCh-EVDHM technique. Then, the total energy and statistical features are extracted from the EEG rhythms. The features are classified into AD and healthy classes using machine learning classifiers. The proposed framework achieved an accuracy of 98.9% and 95.6% in eyes closed and eyes open states, respectively. The proposed framework is compared with the state-of-the-art methods from the literature and found to be more robust, and provides comparable performance measures. Furthermore, the performance of the proposed framework is validated from a combination of EEG signals recorded during eyes open and closed states and achieved an accuracy of 97.3%. The model size of the classifier utilized in the proposed framework is also presented.
{"title":"Detection of Alzheimer's Disease From EEG Signals Using Improved MCh-EVDHM-Based Rhythm Separation","authors":"Vivek Kumar Singh;Ram Bilas Pachori","doi":"10.1109/LSENS.2024.3457243","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3457243","url":null,"abstract":"In this letter, we propose a new framework for Alzheimer's disease (AD) detection using electroencephalogram (EEG) signals. The EEG signals are decomposed into a set of elementary components using improved multichannel eigenvalue decomposition of Hankel matrix (MCh-EVDHM) technique. A rhythm separation method is proposed based on improved MCh-EVDHM technique. Then, the total energy and statistical features are extracted from the EEG rhythms. The features are classified into AD and healthy classes using machine learning classifiers. The proposed framework achieved an accuracy of 98.9% and 95.6% in eyes closed and eyes open states, respectively. The proposed framework is compared with the state-of-the-art methods from the literature and found to be more robust, and provides comparable performance measures. Furthermore, the performance of the proposed framework is validated from a combination of EEG signals recorded during eyes open and closed states and achieved an accuracy of 97.3%. The model size of the classifier utilized in the proposed framework is also presented.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320500","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 : 2024-09-10DOI: 10.1109/LSENS.2024.3457013
Mariana Silveira;Arnaldo Leal-Junior;Wilfried Blanc;Camilo A. R. Diaz
The transmission–reflection analysis (TRA) is a highly cost-effective distributed sensing technique that monitors the transmitted and backscattered powers of a waveguide. Originally, the TRA was proposed and analytically formulated for single-mode optical fibers (SMFs). However, nanoparticle-doped optical fibers (NPFs) have been currently explored to increase the spatial resolution at the cost of diminishing the sensing range. Due to nonlinearities in Rayleigh backscattering (RBS), the mathematical assumptions made by the traditional SMF model cannot be applied to NPFs. Artificial intelligence has already been applied to a NPF-based TRA system to convert intensity to distance in a quasi-distributed configuration. To exploit NFPs for distributed sensing, this letter presents a method to convert intensity to distance. When strong disturbances were induced on fiber, the method exhibited an error up to 5 cm for a sensing range up to 3 m. For weak disturbances, relative errors up to 14.3 cm were obtained. Adding the noise of the acquisition system, the method yielded errors up to 29.24 cm for a 5.4 m sensor (5.41%).
{"title":"Transmission–Reflection Analysis Using Nanoparticle-Doped Fibers: A Method for Intensity-to-Distance Conversion","authors":"Mariana Silveira;Arnaldo Leal-Junior;Wilfried Blanc;Camilo A. R. Diaz","doi":"10.1109/LSENS.2024.3457013","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3457013","url":null,"abstract":"The transmission–reflection analysis (TRA) is a highly cost-effective distributed sensing technique that monitors the transmitted and backscattered powers of a waveguide. Originally, the TRA was proposed and analytically formulated for single-mode optical fibers (SMFs). However, nanoparticle-doped optical fibers (NPFs) have been currently explored to increase the spatial resolution at the cost of diminishing the sensing range. Due to nonlinearities in Rayleigh backscattering (RBS), the mathematical assumptions made by the traditional SMF model cannot be applied to NPFs. Artificial intelligence has already been applied to a NPF-based TRA system to convert intensity to distance in a quasi-distributed configuration. To exploit NFPs for distributed sensing, this letter presents a method to convert intensity to distance. When strong disturbances were induced on fiber, the method exhibited an error up to 5 cm for a sensing range up to 3 m. For weak disturbances, relative errors up to 14.3 cm were obtained. Adding the noise of the acquisition system, the method yielded errors up to 29.24 cm for a 5.4 m sensor (5.41%).","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368495","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 : 2024-09-10DOI: 10.1109/LSENS.2024.3453558
V. Mallikarjuna Reddy M;P. S. Pandian;Karthick P A
Recent advancements and developments in the field of rehabi- litation lead to the invention of myoelectric control interfaces for patients with disabilities. However, decoding the motion intent from the surface electromyography (sEMG) signals of hamstrings and quadriceps is challenging due to its complex mechanics associated with weight bearing joints and stochastic, nonstationary, and multicomponent behavior of signals. In this letter, a novel approach is proposed for multiclass gait phase classification during level walking using temporal convolutional network (TCN) of sEMG signals. For this purpose, sEMG and inertial measurement unit (IMU) data were recorded concurrently from 20 healthy participants during level walking on treadmill at a speed of 2.5 km/h. sEMG were collected from the muscles, namely, rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and semitendinosus (SEM). The IMU measurements of knee flexion/extension data are utilized for labeling the four phases of gait cycle. The root mean square of sEMG epochs is used to design the TCN framework. The results show that the proposed framework has the ability to differentiate the four classes of gait with a maximum accuracy of 86.00% using the myoelectric activity from all the four muscles. The information from the muscle pairs SEM and VL, and RF and BF, yielded the correct detection rate of 83.00% and 84.00%, respectively. In addition, the accuracy is also improved by 6% with TCN when we compare accuracy obtained through convolutional neural network architecture. The findings suggest that the proposed approach is effective in decoding the motion intent of lower limb muscles, which may lead to the development of precise movement control of lower limb prosthesis.
{"title":"Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements","authors":"V. Mallikarjuna Reddy M;P. S. Pandian;Karthick P A","doi":"10.1109/LSENS.2024.3453558","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3453558","url":null,"abstract":"Recent advancements and developments in the field of rehabi- litation lead to the invention of myoelectric control interfaces for patients with disabilities. However, decoding the motion intent from the surface electromyography (sEMG) signals of hamstrings and quadriceps is challenging due to its complex mechanics associated with weight bearing joints and stochastic, nonstationary, and multicomponent behavior of signals. In this letter, a novel approach is proposed for multiclass gait phase classification during level walking using temporal convolutional network (TCN) of sEMG signals. For this purpose, sEMG and inertial measurement unit (IMU) data were recorded concurrently from 20 healthy participants during level walking on treadmill at a speed of 2.5 km/h. sEMG were collected from the muscles, namely, rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and semitendinosus (SEM). The IMU measurements of knee flexion/extension data are utilized for labeling the four phases of gait cycle. The root mean square of sEMG epochs is used to design the TCN framework. The results show that the proposed framework has the ability to differentiate the four classes of gait with a maximum accuracy of 86.00% using the myoelectric activity from all the four muscles. The information from the muscle pairs SEM and VL, and RF and BF, yielded the correct detection rate of 83.00% and 84.00%, respectively. In addition, the accuracy is also improved by 6% with TCN when we compare accuracy obtained through convolutional neural network architecture. The findings suggest that the proposed approach is effective in decoding the motion intent of lower limb muscles, which may lead to the development of precise movement control of lower limb prosthesis.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274963","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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