Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046469
Yanbing Liu, Liping Chen, J. Ding
PINNs, as a new method for solving PDEs, can embed PDEs as a prior into neural networks for training. The distribution of sample residual points has a strong influence on the solution accuracy of PINNs. In this paper, we propose an adaptive sampling algorithm based on the residuals and its gradient characters (Grad-RAR), which utilizes the residuals of sample points to obtain their gradient information and retain sample residual points with special gradients, and combines it with a probabilistic sampling model (RAR-D) to achieve effective sampling in the computational domain. We test the performance of multiple sampling methods for two forward problems and one inverse problem, and the study shows that our proposed adaptive sampling method performs better compared to existing sampling methods.
{"title":"Grad-RAR: An Adaptive Sampling Method Based on Residual Gradient for Physical-Informed Neural Networks","authors":"Yanbing Liu, Liping Chen, J. Ding","doi":"10.1109/ICARCE55724.2022.10046469","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046469","url":null,"abstract":"PINNs, as a new method for solving PDEs, can embed PDEs as a prior into neural networks for training. The distribution of sample residual points has a strong influence on the solution accuracy of PINNs. In this paper, we propose an adaptive sampling algorithm based on the residuals and its gradient characters (Grad-RAR), which utilizes the residuals of sample points to obtain their gradient information and retain sample residual points with special gradients, and combines it with a probabilistic sampling model (RAR-D) to achieve effective sampling in the computational domain. We test the performance of multiple sampling methods for two forward problems and one inverse problem, and the study shows that our proposed adaptive sampling method performs better compared to existing sampling methods.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129070364","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046573
Xiaotong Liu, Jiayuan Zhao, Siyu Wang, Guangying Pei, S. Funahashi, Tianyi Yan
Individual difference is the main factor affecting the effect of emotion regulation neurofeedback training. An individual-specific emotion recognition model can be constructed based on machine learning. However, the current researches simply the preprocessing process to meet real-time feedback, resulting in a reduction in classification accuracy. This paper proposes a closed-loop electroencephalogram (EEG) neurofeedback processing program with high accuracy in feedback information. Artifact subspace reconstruction is used to optimize EEG processing. The positive, neutral, and negative emotion topographic maps of the 5 frequency bands verify inter-individual differences. A support vector machine with particle swarm optimization is used to construct an individual emotion recognition model based on the power spectral density features. The average classification accuracy of 5 subjects is 97.49%. The emotion facial Go/No-go task objectively demonstrates the effectiveness of neurofeedback training on emotion regulation. The closed-loop individual-specific EEG neurofeedback program provides a promising method for emotion regulation training.
{"title":"Closed-loop Individual-specific EEG Neurofeedback for Emotion Regulation","authors":"Xiaotong Liu, Jiayuan Zhao, Siyu Wang, Guangying Pei, S. Funahashi, Tianyi Yan","doi":"10.1109/ICARCE55724.2022.10046573","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046573","url":null,"abstract":"Individual difference is the main factor affecting the effect of emotion regulation neurofeedback training. An individual-specific emotion recognition model can be constructed based on machine learning. However, the current researches simply the preprocessing process to meet real-time feedback, resulting in a reduction in classification accuracy. This paper proposes a closed-loop electroencephalogram (EEG) neurofeedback processing program with high accuracy in feedback information. Artifact subspace reconstruction is used to optimize EEG processing. The positive, neutral, and negative emotion topographic maps of the 5 frequency bands verify inter-individual differences. A support vector machine with particle swarm optimization is used to construct an individual emotion recognition model based on the power spectral density features. The average classification accuracy of 5 subjects is 97.49%. The emotion facial Go/No-go task objectively demonstrates the effectiveness of neurofeedback training on emotion regulation. The closed-loop individual-specific EEG neurofeedback program provides a promising method for emotion regulation training.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116267935","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046496
Huahuan Zhu, Xiaoyu Zhang
This paper takes armored vehicles fire control system as an example, based on the firing table fitting function, this paper discusses the method of calculating the firing elements in the angular rate fire control system and the line rate fire control system. In this paper, the advantages and disadvantages of the traditional method, the angular velocity method and the relative motion method, are compared, and a new method, the combined velocity method, is proposed in the on-line rate fire control system. The accuracy and practicability of the combined velocity method are proved by theoretical analysis and MATLAB simulation.
{"title":"Analysis of the Methods of Solving the Firing Data on Move","authors":"Huahuan Zhu, Xiaoyu Zhang","doi":"10.1109/ICARCE55724.2022.10046496","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046496","url":null,"abstract":"This paper takes armored vehicles fire control system as an example, based on the firing table fitting function, this paper discusses the method of calculating the firing elements in the angular rate fire control system and the line rate fire control system. In this paper, the advantages and disadvantages of the traditional method, the angular velocity method and the relative motion method, are compared, and a new method, the combined velocity method, is proposed in the on-line rate fire control system. The accuracy and practicability of the combined velocity method are proved by theoretical analysis and MATLAB simulation.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118993","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046443
Tong Zheng, L. Liu, Qimeng Chen, Zhongze Chen, Longji Yu, Z. Zhao
Semantic segmentation of images can play an important role in land use, building extraction, road extraction and vehicle detection using remote sensing images. In the scenario of applying Panoptic FPN algorithm for remote sensing images, we found that the decoder of this algorithm is not robust in feature extraction and cannot do weighted enhancement of key spaces and channels, and its encoder loses a lot of high-dimensional semantic features when simply fusing semantic information at all levels by simple addition. To address these two problems, we propose the Se-Resnext encoder based on the attention mechanism and the Concat-based feature fusion mechanism, respectively, and verify the effectiveness of the methods through experiments. The accuracy of semantic segmentation is improved in both suichang dataset and posdam dataset.
{"title":"Semantic Segmentation Algorithm of Remote Sensing Images Based on Improved Panoptic","authors":"Tong Zheng, L. Liu, Qimeng Chen, Zhongze Chen, Longji Yu, Z. Zhao","doi":"10.1109/ICARCE55724.2022.10046443","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046443","url":null,"abstract":"Semantic segmentation of images can play an important role in land use, building extraction, road extraction and vehicle detection using remote sensing images. In the scenario of applying Panoptic FPN algorithm for remote sensing images, we found that the decoder of this algorithm is not robust in feature extraction and cannot do weighted enhancement of key spaces and channels, and its encoder loses a lot of high-dimensional semantic features when simply fusing semantic information at all levels by simple addition. To address these two problems, we propose the Se-Resnext encoder based on the attention mechanism and the Concat-based feature fusion mechanism, respectively, and verify the effectiveness of the methods through experiments. The accuracy of semantic segmentation is improved in both suichang dataset and posdam dataset.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127297129","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046450
Yuan Shi
From the perspective of the world, the problem of water pollution and clean water shortage is increasingly increasing, among which rural water pollution has become a major source of river and lake pollution in the world. Due to the rural farmland runoff, farmland drainage and groundwater infiltration, the main cause of water pollution, which has had a great impact on the rural water environment and ecological environment. Therefore, the monitoring and treatment of rural sewage is a big difficult problem. In recent years, universities and scientific research institutions from all over the world have been committed to the treatment of agricultural sewage and intelligent system research, and have achieved effective results. Among them, the automatic control system with computing as the core has been widely used in the modern era. This paper mainly proposes a set of rural sewage automatic monitoring system based on the Internet of Things.
{"title":"Rural Sewage Automatic Monitoring System Based on the Internet of Things","authors":"Yuan Shi","doi":"10.1109/ICARCE55724.2022.10046450","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046450","url":null,"abstract":"From the perspective of the world, the problem of water pollution and clean water shortage is increasingly increasing, among which rural water pollution has become a major source of river and lake pollution in the world. Due to the rural farmland runoff, farmland drainage and groundwater infiltration, the main cause of water pollution, which has had a great impact on the rural water environment and ecological environment. Therefore, the monitoring and treatment of rural sewage is a big difficult problem. In recent years, universities and scientific research institutions from all over the world have been committed to the treatment of agricultural sewage and intelligent system research, and have achieved effective results. Among them, the automatic control system with computing as the core has been widely used in the modern era. This paper mainly proposes a set of rural sewage automatic monitoring system based on the Internet of Things.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131569039","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}
Smart agriculture is the integration and development of modern science and technology in agriculture related fields. Traditional agriculture is gradually transitioning to automatic and intelligent agriculture. Agricultural Internet of things can not only increase crop output, but also liberate productivity and improve labor return. Therefore, this paper studies the intelligent agrometeorological information monitoring system, which can wirelessly monitor the value and range of temperature and humidity, light, soil humidity, CO2 concentration, and save manpower and material resources for people to monitor agrometeorological information at all time. The intelligent agricultural meteorological information monitoring system uses ZigBee technology conforming to IEEE 802.15.4 standard to deploy wireless networks and each node in the network communicates with each other, The terminal sensor sends the collected temperature and humidity, illumination, soil humidity and CO2 concentration signals to the nearest routing node. The routing node wirelessly sends various signals to the coordinator, which then uploads them to the PC through the serial port. The PC displays all information and gives an alarm according to the designed limit value. This paper focuses on the analysis of the main framework and key technologies of the intelligent agriculture remote monitoring system, and the system research, design and implementation.
{"title":"Research on Intelligent Agricultural Meteorological Information Monitoring and Alarm System","authors":"Kaiyi Liu, Hengyuan Kang, Mingrui Lan, Fan Zhang, Linlin Wan, Hongmei Zhang","doi":"10.1109/ICARCE55724.2022.10046514","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046514","url":null,"abstract":"Smart agriculture is the integration and development of modern science and technology in agriculture related fields. Traditional agriculture is gradually transitioning to automatic and intelligent agriculture. Agricultural Internet of things can not only increase crop output, but also liberate productivity and improve labor return. Therefore, this paper studies the intelligent agrometeorological information monitoring system, which can wirelessly monitor the value and range of temperature and humidity, light, soil humidity, CO2 concentration, and save manpower and material resources for people to monitor agrometeorological information at all time. The intelligent agricultural meteorological information monitoring system uses ZigBee technology conforming to IEEE 802.15.4 standard to deploy wireless networks and each node in the network communicates with each other, The terminal sensor sends the collected temperature and humidity, illumination, soil humidity and CO2 concentration signals to the nearest routing node. The routing node wirelessly sends various signals to the coordinator, which then uploads them to the PC through the serial port. The PC displays all information and gives an alarm according to the designed limit value. This paper focuses on the analysis of the main framework and key technologies of the intelligent agriculture remote monitoring system, and the system research, design and implementation.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131360248","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}
In the swarm formation control system by leader-follower strategy, obstacle avoidance is the key requirement of unmanned aerial vehicle (UAV) swarm to coordinate trajectory planning, while the traditional artificial potential field (APF) ignores the problem that the UAV swarm needs to return to the scheduled route immediately after obstacle avoidance. Based on the UAV obstacle avoidance rules, this paper proposes a trajectory planning scheme based on improved artificial potential field (IAPF). Though IAPF, the UAV swarm considers the minimum turning radius factor when avoiding obstacles, does not deviate from the route after obstacle avoidance, and returns to the scheduled route nearby, thus the unity of UAV swarm obstacle avoidance and trajectory planning is realized. The simulation results show that the proposed scheme can effectively solve the problem of UAV swarm returning to the scheduled route immediately after obstacle avoidance.
{"title":"Research on Obstacle Avoidance Algorithm of Fixed-wing UAV Swarms Based on Improved Artificial Potential Field","authors":"Qiping Zhou, Yong Wei, Wei He, Shu-min Shang, Haibo Fan, Weisong Yin","doi":"10.1109/ICARCE55724.2022.10046495","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046495","url":null,"abstract":"In the swarm formation control system by leader-follower strategy, obstacle avoidance is the key requirement of unmanned aerial vehicle (UAV) swarm to coordinate trajectory planning, while the traditional artificial potential field (APF) ignores the problem that the UAV swarm needs to return to the scheduled route immediately after obstacle avoidance. Based on the UAV obstacle avoidance rules, this paper proposes a trajectory planning scheme based on improved artificial potential field (IAPF). Though IAPF, the UAV swarm considers the minimum turning radius factor when avoiding obstacles, does not deviate from the route after obstacle avoidance, and returns to the scheduled route nearby, thus the unity of UAV swarm obstacle avoidance and trajectory planning is realized. The simulation results show that the proposed scheme can effectively solve the problem of UAV swarm returning to the scheduled route immediately after obstacle avoidance.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131776206","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046575
Yihui Zhang, Yin Zhang, Lihua Wang, Xuan Dong, Yijie Li, Hang Sun, Xiaomei Yang
Crack detection of electrical equipment is significant to maintain its normal operation. Many methods based on deep learning have been applied to detect cracks from the captured images, while most of the existing crack detection algorithms cannot detect the crack quickly and effectively, and rarely applied in electrical equipment with complex structures. In this paper, an improved GoogLeNet by combining DenseBlock and feature fusion layer is proposed. To reduce the amount of network training parameters, DenseBlock is utilized to replace the two branches with a large size convolution kernel in Inception model of the classical GoogLeNet. Moreover, to improve the detection accuracy of the network, a fusion layer integrating deep and shallow features is introduced in the improved GoogLeNet. To mitigate the issue of limited amount of training image data of electrical equipment, except for data augmentation, a transfer learning strategy is used to initialize the parameters of the improved GoogLeNet, where the initial parameters are obtained from the results of training public crack datasets. The experimental results show that the improved GoogLeNet can effectively detect the crack of electrical equipment, and the detection accuracy reaches 97.06%.
{"title":"Crack Detection of Electrical Equipment Based on Improved GoogLeNet","authors":"Yihui Zhang, Yin Zhang, Lihua Wang, Xuan Dong, Yijie Li, Hang Sun, Xiaomei Yang","doi":"10.1109/ICARCE55724.2022.10046575","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046575","url":null,"abstract":"Crack detection of electrical equipment is significant to maintain its normal operation. Many methods based on deep learning have been applied to detect cracks from the captured images, while most of the existing crack detection algorithms cannot detect the crack quickly and effectively, and rarely applied in electrical equipment with complex structures. In this paper, an improved GoogLeNet by combining DenseBlock and feature fusion layer is proposed. To reduce the amount of network training parameters, DenseBlock is utilized to replace the two branches with a large size convolution kernel in Inception model of the classical GoogLeNet. Moreover, to improve the detection accuracy of the network, a fusion layer integrating deep and shallow features is introduced in the improved GoogLeNet. To mitigate the issue of limited amount of training image data of electrical equipment, except for data augmentation, a transfer learning strategy is used to initialize the parameters of the improved GoogLeNet, where the initial parameters are obtained from the results of training public crack datasets. The experimental results show that the improved GoogLeNet can effectively detect the crack of electrical equipment, and the detection accuracy reaches 97.06%.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121102454","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046473
Xiyuan Jiang, Zihan Tang, Bo Ou, Jianqin Xiong
Reversible data hiding (RDH) for medical image contrast enhancement is designed to effectively improve the quality of medical images to help doctors make correct diagnosis, while addressing issues of privacy protection and image content integrity. In this paper, we propose a new RDH method for medical image contrast enhancement. To enhance the edge contour of medical image, we employ the superpixel segmentation to identify region of interest (ROI), and then improve the region contrast to facilitate the diagnosis. A new histogram modification is proposed to achieve a local histogram equalization effect. Two adjacent bins with the largest difference in number are selected for expansion, in order to spread the histogram evenly as much as possible. In addition, the histogram modification is adaptive to the expansion bins by using the multiple modification manner, and can spread out the highly populated bins more evenly. Experimental results verify that, compared with the existing typical methods, the proposed method can better improve the medical image quality after data embedding in terms of contrast.
{"title":"Dynamic Reversible Data Hiding for Edge Contrast Enhancement of Medical Image","authors":"Xiyuan Jiang, Zihan Tang, Bo Ou, Jianqin Xiong","doi":"10.1109/ICARCE55724.2022.10046473","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046473","url":null,"abstract":"Reversible data hiding (RDH) for medical image contrast enhancement is designed to effectively improve the quality of medical images to help doctors make correct diagnosis, while addressing issues of privacy protection and image content integrity. In this paper, we propose a new RDH method for medical image contrast enhancement. To enhance the edge contour of medical image, we employ the superpixel segmentation to identify region of interest (ROI), and then improve the region contrast to facilitate the diagnosis. A new histogram modification is proposed to achieve a local histogram equalization effect. Two adjacent bins with the largest difference in number are selected for expansion, in order to spread the histogram evenly as much as possible. In addition, the histogram modification is adaptive to the expansion bins by using the multiple modification manner, and can spread out the highly populated bins more evenly. Experimental results verify that, compared with the existing typical methods, the proposed method can better improve the medical image quality after data embedding in terms of contrast.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115051016","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046588
Gang-song Dong, C. Li
Smart wearable devices have emerged in response to the situation, providing a new method for people's health monitoring. Three kinds of intelligent sensors have been selected to adopt the modular optimization design idea, and the wireless motion sensor node system and data display interface have been designed, which can collect and record heart rate, body temperature, movement steps and other information in real time and effectively. Based on ADS1292 analog front-end chip design ECG detection circuit, through the difference threshold method to improve detection accuracy, complete real-time collection and recording of user's heart rate, achieve dynamic ECG test and display, analysis and calculation of user's heart rate, the error is not more than 5%. Push-pull LMT70 temperature sensor is used to measure the body temperature, which increases the carrying capacity and keeps the sampling rate no less than 10 times/min. Three high-resolution ADXL355 acceleration sensors are added, and the interval sampling method is adopted to calculate the number of moving steps and moving distance, and the error is kept less than 5%. At the same time, the wireless motion sensor node has the function of surfing the Internet and transmitting data to the server to complete the data operation. After debugging and analysis for many times, the system has reached the design requirements, and has the advantages of low cost, high precision, easy to use and low power consumption.
{"title":"Design and Implementation of Wireless Motion Sensor Node System","authors":"Gang-song Dong, C. Li","doi":"10.1109/ICARCE55724.2022.10046588","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046588","url":null,"abstract":"Smart wearable devices have emerged in response to the situation, providing a new method for people's health monitoring. Three kinds of intelligent sensors have been selected to adopt the modular optimization design idea, and the wireless motion sensor node system and data display interface have been designed, which can collect and record heart rate, body temperature, movement steps and other information in real time and effectively. Based on ADS1292 analog front-end chip design ECG detection circuit, through the difference threshold method to improve detection accuracy, complete real-time collection and recording of user's heart rate, achieve dynamic ECG test and display, analysis and calculation of user's heart rate, the error is not more than 5%. Push-pull LMT70 temperature sensor is used to measure the body temperature, which increases the carrying capacity and keeps the sampling rate no less than 10 times/min. Three high-resolution ADXL355 acceleration sensors are added, and the interval sampling method is adopted to calculate the number of moving steps and moving distance, and the error is kept less than 5%. At the same time, the wireless motion sensor node has the function of surfing the Internet and transmitting data to the server to complete the data operation. After debugging and analysis for many times, the system has reached the design requirements, and has the advantages of low cost, high precision, easy to use and low power consumption.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129906165","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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