Pub Date : 2024-12-20DOI: 10.1109/LSENS.2024.3520018
Yi-Bing Lin;Yi-Ting Chen;Wan-Jung Hsieh;Wen-Liang Chen;Yun-Wei Lin;Edward Sun
The Phalaenopsis orchid is highly valued in the ornamental flower market and is primarily cultivated in greenhouses. In a traditional commercial greenhouse, farmers must manually check daily for any signs of disease among the plants. Sick plants must be removed immediately to prevent the spread of diseases to healthy ones. In precision agriculture, farmers are expected to be alerted when a certain percentage (e.g., less than 2%) of the plants are infected so that they can be removed at the right time. Many experiments have been conducted in laboratories with constant temperature and humidity to investigate the spore germination rate, where spores typically germinate within a few days. However, these findings cannot be directly applied to large-scale greenhouses with long growth periods (over 200 days) and varying temperatures and humidity. The contribution of this letter is that we are the first to propose a sensor specifically designed for use in large-scale greenhouse environments to determine the spore germination rate for orchids. We have designed a simple yet novel algorithm to dynamically calibrate the spore germination sensor. Our experiments indicate that with the calibrated spore germination sensor, the outbreak probability can be completely eliminated, and human checking overhead can be reduced by up to 97.8%.
{"title":"Design of a Spore Germination Sensor for Orchids","authors":"Yi-Bing Lin;Yi-Ting Chen;Wan-Jung Hsieh;Wen-Liang Chen;Yun-Wei Lin;Edward Sun","doi":"10.1109/LSENS.2024.3520018","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3520018","url":null,"abstract":"The Phalaenopsis orchid is highly valued in the ornamental flower market and is primarily cultivated in greenhouses. In a traditional commercial greenhouse, farmers must manually check daily for any signs of disease among the plants. Sick plants must be removed immediately to prevent the spread of diseases to healthy ones. In precision agriculture, farmers are expected to be alerted when a certain percentage (e.g., less than 2%) of the plants are infected so that they can be removed at the right time. Many experiments have been conducted in laboratories with constant temperature and humidity to investigate the spore germination rate, where spores typically germinate within a few days. However, these findings cannot be directly applied to large-scale greenhouses with long growth periods (over 200 days) and varying temperatures and humidity. The contribution of this letter is that we are the first to propose a sensor specifically designed for use in large-scale greenhouse environments to determine the spore germination rate for orchids. We have designed a simple yet novel algorithm to dynamically calibrate the spore germination sensor. Our experiments indicate that with the calibrated spore germination sensor, the outbreak probability can be completely eliminated, and human checking overhead can be reduced by up to 97.8%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976134","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 : 2024-12-19DOI: 10.1109/LSENS.2024.3520524
Shouharda Ghosh;Nithin George
Direction of arrival (DOA) estimation techniques are essential for determining the locations of signal sources using sensor arrays. For a uniform linear array, the number of detectable sources is limited to one less than the number of sensors. Sparse linear arrays overcome this limitation by leveraging the difference array to estimate more sources than sensors. However, gain and phase mismatches among sensors can impair accuracy. Existing algorithms to correct these mismatches are computationally demanding, making them unsuitable for low-power Internet-of-Things (IoT) devices. This article proposes a novel method to integrate gain-phase compensation into adaptive filtering-based DOA estimation algorithms. The proposed approach reduces computational complexity and improves performance, especially in low SNR and low snapshot scenarios, facilitating efficient deployment in low-power devices.
{"title":"Low Complexity Gain-Phase Error Correction for Adaptive Underdetermined DOA Estimation in Sensor Arrays","authors":"Shouharda Ghosh;Nithin George","doi":"10.1109/LSENS.2024.3520524","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3520524","url":null,"abstract":"Direction of arrival (DOA) estimation techniques are essential for determining the locations of signal sources using sensor arrays. For a uniform linear array, the number of detectable sources is limited to one less than the number of sensors. Sparse linear arrays overcome this limitation by leveraging the difference array to estimate more sources than sensors. However, gain and phase mismatches among sensors can impair accuracy. Existing algorithms to correct these mismatches are computationally demanding, making them unsuitable for low-power Internet-of-Things (IoT) devices. This article proposes a novel method to integrate gain-phase compensation into adaptive filtering-based DOA estimation algorithms. The proposed approach reduces computational complexity and improves performance, especially in low SNR and low snapshot scenarios, facilitating efficient deployment in low-power devices.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905738","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 : 2024-12-19DOI: 10.1109/LSENS.2024.3520408
Amin Biglari;Qisong Hu;Wei Tang
In this letter, we present a pixel-level predictive sampling method for image sensing and processing to reduce the computing overhead for power-limited image sensing systems. The predictive sampling method scans through rows and columns to identify the location and value of the critical pixels, which are the turning points in the row and column arrays. The prediction is performed using the value of prior pixels and a predefined error threshold. When the prediction is successful, the pixel is marked as a noncritical pixel and is skipped for recording and processing. Only the critical pixels are selected for further processing. We proposed reconstruction methods that recover the raw image from the selected critical pixels using interpolation. The experimental results show that the proposed method can reduce the data throughput by 72% with an error of 1.6% for sparse images. The convolutional neural network model applied with this method can achieve a similar detection accuracy in a standard method while only using 27.1% of data size.
{"title":"Predictive Sampling in Image Sensing for Sparse Image Processing","authors":"Amin Biglari;Qisong Hu;Wei Tang","doi":"10.1109/LSENS.2024.3520408","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3520408","url":null,"abstract":"In this letter, we present a pixel-level predictive sampling method for image sensing and processing to reduce the computing overhead for power-limited image sensing systems. The predictive sampling method scans through rows and columns to identify the location and value of the critical pixels, which are the turning points in the row and column arrays. The prediction is performed using the value of prior pixels and a predefined error threshold. When the prediction is successful, the pixel is marked as a noncritical pixel and is skipped for recording and processing. Only the critical pixels are selected for further processing. We proposed reconstruction methods that recover the raw image from the selected critical pixels using interpolation. The experimental results show that the proposed method can reduce the data throughput by 72% with an error of 1.6% for sparse images. The convolutional neural network model applied with this method can achieve a similar detection accuracy in a standard method while only using 27.1% of data size.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905786","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 : 2024-12-19DOI: 10.1109/LSENS.2024.3520656
Yan Sun;Shuai Shao;Wen-qin Wang;Maria Sabrina Greco;Fulvio Gini;Shunsheng Zhang
The multidimensional structure of frequency diverse array (FDA) multiple-input–multiple-out (MIMO) radar signals has attracted a lot of attention. It allows to extend conventional space–time adaptive processing to space–time-range adaptive processing (STRAP). In this letter, we propose two tensorial filters for FDA-MIMO-STRAP, called the clutter subspace filter and the clutter-free subspace filter, which exploit the low-rankness of the clutter to achieve better clutter suppression in a small auxiliary training data scenario. The proposed method makes use of the alternative unfolding higher order singular value decomposition with different dimensional partitions. Numerical results demonstrate the effectiveness of the proposed filters over existing low-rank vectorial and tensorial methods.
{"title":"Low-Rank STRAP Filter Via Alternative Unfolding HOSVD for FDA-MIMO Radar","authors":"Yan Sun;Shuai Shao;Wen-qin Wang;Maria Sabrina Greco;Fulvio Gini;Shunsheng Zhang","doi":"10.1109/LSENS.2024.3520656","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3520656","url":null,"abstract":"The multidimensional structure of frequency diverse array (FDA) multiple-input–multiple-out (MIMO) radar signals has attracted a lot of attention. It allows to extend conventional space–time adaptive processing to space–time-range adaptive processing (STRAP). In this letter, we propose two tensorial filters for FDA-MIMO-STRAP, called the clutter subspace filter and the clutter-free subspace filter, which exploit the low-rankness of the clutter to achieve better clutter suppression in a small auxiliary training data scenario. The proposed method makes use of the alternative unfolding higher order singular value decomposition with different dimensional partitions. Numerical results demonstrate the effectiveness of the proposed filters over existing low-rank vectorial and tensorial methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993372","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 : 2024-12-17DOI: 10.1109/LSENS.2024.3519391
Mark Kantor;Nicola Molinazzi;Tsvi Shmilovich;Slava Krylov
We report on the design, fabrication, and experimental functionality demonstration of a simple, manufacturable, and cost-effective polymeric vibration intensity monitoring sensor for industrial applications. In the device combining sensing, energy harvesting, data processing, edge computing, and wireless connectivity functionalities, the electromagnetic harvester's output is used for the vibration intensity sensing. The electromechanical core of the device is realized as an assembly of three free-standing polyethylene terephthalate membranes with an array of micromagnets attached to them. The vibration of the magnets in proximity to the microcoils induces an electric current in the circuit and enables the EEPROM bit writing operation. The number of the on / off voltage switching and memory writing events in unit time, each corresponding to the stored energy threshold level crossing, is used as a condition monitoring indicator. The output voltage of 1.2 V pp (peak to peak) was measured in the 3 mm thick and 30 mm in diameter harvester operated at the accelerations of ≈31 g and frequencies between 860 and 930 Hz. The feasibility of the sensor operational cycle, including energy harvesting and storage, memory writing, and wireless data reading, was demonstrated.
{"title":"Low-Cost Polymeric Energy Harvester as Vibration Intensity Sensor","authors":"Mark Kantor;Nicola Molinazzi;Tsvi Shmilovich;Slava Krylov","doi":"10.1109/LSENS.2024.3519391","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3519391","url":null,"abstract":"We report on the design, fabrication, and experimental functionality demonstration of a simple, manufacturable, and cost-effective polymeric vibration intensity monitoring sensor for industrial applications. In the device combining sensing, energy harvesting, data processing, edge computing, and wireless connectivity functionalities, the electromagnetic harvester's output is used for the vibration intensity sensing. The electromechanical core of the device is realized as an assembly of three free-standing polyethylene terephthalate membranes with an array of micromagnets attached to them. The vibration of the magnets in proximity to the microcoils induces an electric current in the circuit and enables the EEPROM bit writing operation. The number of the \u0000<sc>on</small>\u0000/\u0000<sc>off</small>\u0000 voltage switching and memory writing events in unit time, each corresponding to the stored energy threshold level crossing, is used as a condition monitoring indicator. The output voltage of 1.2 V\u0000<sub>pp</sub>\u0000 (peak to peak) was measured in the 3 mm thick and 30 mm in diameter harvester operated at the accelerations of ≈31 g and frequencies between 860 and 930 Hz. The feasibility of the sensor operational cycle, including energy harvesting and storage, memory writing, and wireless data reading, was demonstrated.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880429","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}
Molecular ferroelectrics are garnering growing interest across various applications and have only recently been utilized in triboelectric nanogenerators (TENGs). However, their use has largely been confined to uniaxial ferroelectrics, with multiaxial ferroelectrics remaining underexplored. In this letter, we introduce a newly constructed TENG based on a multiaxial ferroelectric material, 3,3-difluorocyclobutanammonium hydrochloride [(3,3-DFCBA)Cl], which showcases distinctive traits of multiaxial ferroelectricity, a strong piezoelectric coefficient ( d 33 ) of 33 pC/N, and a remarkable piezoelectric voltage coefficient of ( g 33 ) of 437.2 x 10 -3 Vm/N, about twice than that of polyvinylidene difluoride, making it highly suitable for the emerging field of wearable sensors. Herein, the multiaxial ferroelectric material (3,3-DFCBA)Cl was incorporated into a polydimethylsiloxane (PDMS) composite, with electrospun polyvinyl alcohol fibers serving as the positive triboelectric layer. The resulting TENG device achieved the highest output voltage of 233 V and a maximum power density of 361 mW/m 2 under an optimal load of 20 MΩ for the optimized 10 wt% PDMS/(3,3-DFCBA)Cl composite device. In addition, the harvested energy proved effective for capacitor charging applications.
{"title":"Triboelectric Nanogenerator With Hybrid Polymer Composites Based on Multiaxial Molecular Ferroelectric","authors":"Swati Deswal;Nanfei He;Wei Gao;Bongmook Lee;Veena Misra","doi":"10.1109/LSENS.2024.3519760","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3519760","url":null,"abstract":"Molecular ferroelectrics are garnering growing interest across various applications and have only recently been utilized in triboelectric nanogenerators (TENGs). However, their use has largely been confined to uniaxial ferroelectrics, with multiaxial ferroelectrics remaining underexplored. In this letter, we introduce a newly constructed TENG based on a multiaxial ferroelectric material, 3,3-difluorocyclobutanammonium hydrochloride [(3,3-DFCBA)Cl], which showcases distinctive traits of multiaxial ferroelectricity, a strong piezoelectric coefficient (\u0000<italic>d</i>\u0000<sub>33</sub>\u0000) of 33 pC/N, and a remarkable piezoelectric voltage coefficient of (\u0000<italic>g</i>\u0000<sub>33</sub>\u0000) of 437.2 x 10\u0000<sup>-3</sup>\u0000 Vm/N, about twice than that of polyvinylidene difluoride, making it highly suitable for the emerging field of wearable sensors. Herein, the multiaxial ferroelectric material (3,3-DFCBA)Cl was incorporated into a polydimethylsiloxane (PDMS) composite, with electrospun polyvinyl alcohol fibers serving as the positive triboelectric layer. The resulting TENG device achieved the highest output voltage of 233 V and a maximum power density of 361 mW/m\u0000<sup>2</sup>\u0000 under an optimal load of 20 MΩ for the optimized 10 wt% PDMS/(3,3-DFCBA)Cl composite device. In addition, the harvested energy proved effective for capacitor charging applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912408","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 : 2024-12-17DOI: 10.1109/LSENS.2024.3519719
Jintu Borah;Tanujit Chakraborty;Md. Shahrul Md. Nadzir;Mylene G. Cayetano;Francesco Benedetto;Shubhankar Majumdar
Accurate and reliable air quality forecasting is essential for protecting public health, sustainable development, pollution control, and enhanced urban planning. This letter proposes a novel architecture namely wavelet-based CatBoost to forecast the real-time concentrations of air pollutants by combining the maximal overlapping discrete wavelet transform with the CatBoost model. This hybrid approach efficiently transforms time series of air pollution concentration levels into high-frequency and low-frequency components, thereby extracting signal from noise and improving prediction accuracy and robustness. Evaluation of two distinct regional datasets, from the Central Air Pollution Control Board sensor network and a low-cost air quality sensor system, underscores the superior performance of our proposed methodology in real-time forecasting compared to the state-of-the-art machine learning and deep learning architectures.
{"title":"A Novel Hybrid Approach For Efficiently Forecasting Air Quality Data","authors":"Jintu Borah;Tanujit Chakraborty;Md. Shahrul Md. Nadzir;Mylene G. Cayetano;Francesco Benedetto;Shubhankar Majumdar","doi":"10.1109/LSENS.2024.3519719","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3519719","url":null,"abstract":"Accurate and reliable air quality forecasting is essential for protecting public health, sustainable development, pollution control, and enhanced urban planning. This letter proposes a novel architecture namely wavelet-based CatBoost to forecast the real-time concentrations of air pollutants by combining the maximal overlapping discrete wavelet transform with the CatBoost model. This hybrid approach efficiently transforms time series of air pollution concentration levels into high-frequency and low-frequency components, thereby extracting signal from noise and improving prediction accuracy and robustness. Evaluation of two distinct regional datasets, from the Central Air Pollution Control Board sensor network and a low-cost air quality sensor system, underscores the superior performance of our proposed methodology in real-time forecasting compared to the state-of-the-art machine learning and deep learning architectures.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918307","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 : 2024-12-16DOI: 10.1109/LSENS.2024.3518433
Eudald Sangenis;Chi-Shih Jao;Crystal Wai;Andrei M. Shkel
Accurately localizing points of interest is vital for firefighters and first responders for effective surveying and rescue missions. Traditionally, firefighters rely on subjective visual descriptions transmitted via radios, leading to time-consuming and error-prone communication about their locations. This article presents an approach for far target detection (FTD) provided in terms of longitude, latitude, and altitude (LLA) coordinates in environments where GPS signals may not be available. Using LLA coordinates ensures concise communication among team members on their locations and provides a common coordinate reference system between outdoors/indoors. This article discusses the integration of three devices as a foundation for the FTD system. First, it uses zero-velocity-update (ZUPT)-aided inertial navigation systems (INS) via a foot-mounted inertial measurement unit (IMU) for personnel localization. Second, an augmented reality (AR) headset is employed to localize a handheld platform (HP) relative to the foot. Third, HP is used to determine the direction the firefighter is pointing at and to measure the distance to the objects. The system's performance was assessed through five experiments where a subject mapped a static point while walking a straight path demonstrating that the system is capable of achieving mapping precision within 2 [m] for distances on the order of 20 [m] from the target.
{"title":"Far-Target Detection System for Outdoor and Indoor Environments","authors":"Eudald Sangenis;Chi-Shih Jao;Crystal Wai;Andrei M. Shkel","doi":"10.1109/LSENS.2024.3518433","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3518433","url":null,"abstract":"Accurately localizing points of interest is vital for firefighters and first responders for effective surveying and rescue missions. Traditionally, firefighters rely on subjective visual descriptions transmitted via radios, leading to time-consuming and error-prone communication about their locations. This article presents an approach for far target detection (FTD) provided in terms of longitude, latitude, and altitude (LLA) coordinates in environments where GPS signals may not be available. Using LLA coordinates ensures concise communication among team members on their locations and provides a common coordinate reference system between outdoors/indoors. This article discusses the integration of three devices as a foundation for the FTD system. First, it uses zero-velocity-update (ZUPT)-aided inertial navigation systems (INS) via a foot-mounted inertial measurement unit (IMU) for personnel localization. Second, an augmented reality (AR) headset is employed to localize a handheld platform (HP) relative to the foot. Third, HP is used to determine the direction the firefighter is pointing at and to measure the distance to the objects. The system's performance was assessed through five experiments where a subject mapped a static point while walking a straight path demonstrating that the system is capable of achieving mapping precision within 2 [m] for distances on the order of 20 [m] from the target.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905845","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 : 2024-12-13DOI: 10.1109/LSENS.2024.3517624
Hongxi Liu;Jiaole Wang;Shuang Song
Capsule robots (CRs) with biopsy functions is of great importance in clinical diagnosis. However, localization and sensing method for biopsy CR was still lacking in clinical scenarios. In this study, we provide a novel constraint multimagnet sensing method to locate the biopsy CR and evaluate the pop-out length of the biopsy needle, which is based on multimagnet tracking and constraints from the relationship between the magnets inside the biopsy CR. In-vitro experiments with biopsy CR have been conducted to verify the proposed method. The mean position error was $1.83pm 0.21$ mm. The mean orientation error is $0.013pm 0.004^circ$ . The mean pop-out length error of all the samples is $1.67pm 0.32$ mm. The proposed method can locate the biopsy CR effectively, making it a valuable addition to noninvasive gastrointestinal diagnosis and treatment.
{"title":"Pose and Biopsy Sensing for Capsule Robot Based on Conditioned Multiple Magnets Tracking","authors":"Hongxi Liu;Jiaole Wang;Shuang Song","doi":"10.1109/LSENS.2024.3517624","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3517624","url":null,"abstract":"Capsule robots (CRs) with biopsy functions is of great importance in clinical diagnosis. However, localization and sensing method for biopsy CR was still lacking in clinical scenarios. In this study, we provide a novel constraint multimagnet sensing method to locate the biopsy CR and evaluate the pop-out length of the biopsy needle, which is based on multimagnet tracking and constraints from the relationship between the magnets inside the biopsy CR. In-vitro experiments with biopsy CR have been conducted to verify the proposed method. The mean position error was \u0000<inline-formula><tex-math>$1.83pm 0.21$</tex-math></inline-formula>\u0000 mm. The mean orientation error is \u0000<inline-formula><tex-math>$0.013pm 0.004^circ$</tex-math></inline-formula>\u0000. The mean pop-out length error of all the samples is \u0000<inline-formula><tex-math>$1.67pm 0.32$</tex-math></inline-formula>\u0000 mm. The proposed method can locate the biopsy CR effectively, making it a valuable addition to noninvasive gastrointestinal diagnosis and treatment.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 1","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912436","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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