Pub Date : 2024-11-07DOI: 10.1109/LSENS.2024.3493253
Dhanhanjay Pachori;Tapan Kumar Gandhi
This letter presents an innovative approach based on Fourier–Bessel series expansion (FBSE) in order to identify seizure and normal electroencephalogram (EEG) signals. The different set of FBSE coefficients are used to separate the five EEG rhythms, namely, delta, theta, alpha, beta, and gamma rhythms. Further, images are generated from the matrices obtained after applying the concept of Euclidean distance on the EEG rhythms. The generated images are employed as features for the classification using convolutional neural network. Notably, our proposed methodology achieves 100% accuracy in distinguishing between seizure and normal EEG signals on the publicly available Bonn University EEG database. This robust performance demonstrates the efficacy of our approach in handling complicated EEG signal patterns. The proposed framework for automated classification of epileptic seizure based on EEG rhythms provides information about the behavior of rhythms during epilepsy. The experimental results on the publicly available Bonn University EEG database show the effectiveness of proposed framework. The performance of the proposed framework is also compared with the other existing frameworks from the literature.
{"title":"FBSE-Based Approach for Discriminating Seizure and Normal EEG Signals","authors":"Dhanhanjay Pachori;Tapan Kumar Gandhi","doi":"10.1109/LSENS.2024.3493253","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3493253","url":null,"abstract":"This letter presents an innovative approach based on Fourier–Bessel series expansion (FBSE) in order to identify seizure and normal electroencephalogram (EEG) signals. The different set of FBSE coefficients are used to separate the five EEG rhythms, namely, delta, theta, alpha, beta, and gamma rhythms. Further, images are generated from the matrices obtained after applying the concept of Euclidean distance on the EEG rhythms. The generated images are employed as features for the classification using convolutional neural network. Notably, our proposed methodology achieves 100% accuracy in distinguishing between seizure and normal EEG signals on the publicly available Bonn University EEG database. This robust performance demonstrates the efficacy of our approach in handling complicated EEG signal patterns. The proposed framework for automated classification of epileptic seizure based on EEG rhythms provides information about the behavior of rhythms during epilepsy. The experimental results on the publicly available Bonn University EEG database show the effectiveness of proposed framework. The performance of the proposed framework is also compared with the other existing frameworks from the literature.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713889","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 : 2024-11-06DOI: 10.1109/LSENS.2024.3492373
Winston Doss Marveldoss;Bandaru Joshika;Bijo Sebastian
Accurate perception of the environment, including the detection and tracking of humans, is essential for safe navigation of mobile robots in human-centric environments. Existing State-of-the-Art techniques rely on high-performance sensors. This leads to expensive robotic systems, which limits the large-scale deployment of autonomous mobile robots in social spaces. In this letter, we propose and validate a novel human tracking and estimation approach that relies on a low-cost 2-D LiDAR and a monocular camera. The proposed approach leverages the capabilities of each sensor by relying on the camera for human detection and the LiDAR for human pose estimation. Precise calibration and registration of the sensor frames allow for data association in the presence of multiple human targets. Human detection and pose estimation data from the sensor suite are used as measurement by an extended Kalman filter, which allows for effective tracking over multiple frames, even in the presence of occlusion. The overall approach addresses the limitations of each individual sensor without increasing the overall cost of the sensor suite. Tracking and estimation performance for the proposed approach was evaluated on experimental trails in real-world conditions with artificial markers as ground truth for each human target. The results demonstrate satisfactory performance for the proposed approach to be used in human-aware autonomous navigation in real-world settings.
{"title":"Tracking and Estimation Approach for Human-Aware Mobile Robot Navigation","authors":"Winston Doss Marveldoss;Bandaru Joshika;Bijo Sebastian","doi":"10.1109/LSENS.2024.3492373","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3492373","url":null,"abstract":"Accurate perception of the environment, including the detection and tracking of humans, is essential for safe navigation of mobile robots in human-centric environments. Existing State-of-the-Art techniques rely on high-performance sensors. This leads to expensive robotic systems, which limits the large-scale deployment of autonomous mobile robots in social spaces. In this letter, we propose and validate a novel human tracking and estimation approach that relies on a low-cost 2-D LiDAR and a monocular camera. The proposed approach leverages the capabilities of each sensor by relying on the camera for human detection and the LiDAR for human pose estimation. Precise calibration and registration of the sensor frames allow for data association in the presence of multiple human targets. Human detection and pose estimation data from the sensor suite are used as measurement by an extended Kalman filter, which allows for effective tracking over multiple frames, even in the presence of occlusion. The overall approach addresses the limitations of each individual sensor without increasing the overall cost of the sensor suite. Tracking and estimation performance for the proposed approach was evaluated on experimental trails in real-world conditions with artificial markers as ground truth for each human target. The results demonstrate satisfactory performance for the proposed approach to be used in human-aware autonomous navigation in real-world settings.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679324","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 : 2024-11-06DOI: 10.1109/LSENS.2024.3492333
Cedric Pieters;Tom Verschooten;Grim Keulemans;Liesbet Lagae;Jon Øyvind Kjellman;Xavier Rottenberg;Hilde Jans
Affordable and compact light sources, along with highly sensitive, broadband, low-noise sensors, are essential for enabling point-of-care photoacoustic imaging applications in resource-limited settings. Traditional systems use piezoelectric transducers, which often suffer from limited bandwidth and sensitivity, combined with solid-state lasers that are expensive and bulky. We present a potentially low-cost light-emitting diode (LED)-based photoacoustic imaging system featuring a highly sensitive optomechanical ultrasound sensor operating near thermomechanical noise limits. Utilizing a 620-nm LED, our setup delivers microjoules of pulse energy with 100-ns pulsewidths. We demonstrate its capability to resolve fine details through 2-D scan tomography, with minimal averaging for an effective sample rate of 100 Hz. Future improvements, including the development of larger LED arrays, multiplexed sensors, and on-chip integrated lasers, promise to enhance performance and further expand the technology's applicability.
{"title":"Sensitive Optomechanical Ultrasound Sensor in an LED-Based, Low Fluence Photoacoustic Imaging System","authors":"Cedric Pieters;Tom Verschooten;Grim Keulemans;Liesbet Lagae;Jon Øyvind Kjellman;Xavier Rottenberg;Hilde Jans","doi":"10.1109/LSENS.2024.3492333","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3492333","url":null,"abstract":"Affordable and compact light sources, along with highly sensitive, broadband, low-noise sensors, are essential for enabling point-of-care photoacoustic imaging applications in resource-limited settings. Traditional systems use piezoelectric transducers, which often suffer from limited bandwidth and sensitivity, combined with solid-state lasers that are expensive and bulky. We present a potentially low-cost light-emitting diode (LED)-based photoacoustic imaging system featuring a highly sensitive optomechanical ultrasound sensor operating near thermomechanical noise limits. Utilizing a 620-nm LED, our setup delivers microjoules of pulse energy with 100-ns pulsewidths. We demonstrate its capability to resolve fine details through 2-D scan tomography, with minimal averaging for an effective sample rate of 100 Hz. Future improvements, including the development of larger LED arrays, multiplexed sensors, and on-chip integrated lasers, promise to enhance performance and further expand the technology's applicability.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777527","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 : 2024-11-05DOI: 10.1109/LSENS.2024.3491764
Shreya Dhar;Parthib Paul;Subha Mandal;Gobinda Sen
The design and implementation of a portable, low-cost microwave sensors for a wide range of sensing applications is presented in this letter. The proposed sensor utilizes a planar transmission line with a defected ground structure (DGS) comprise of three folded complementary split ring resonators (CSRRs). The use of CSRR-based DGS makes the design acts as a stop band filter with high quality factor at the resonant frequency of the resonator. The proposed sensor design is very compact in dimensions of $0.214 times 0.16{{lambda }_0}.$