This paper presents CurveLight, an accurate and practical light positioning system. In CurveLight, the signal transmitter includes an infrared LED, covered by a hemispherical and rotatable shade, and the receiver detects the light signals with a photosensitive diode. When the shade is rotating, the transmitter generates a unique sequence of light signals for each point in the covered space. The main novelty of the system design is a set of curves that define different regions, either transparent or translucent, on the shade. The regions allow the light signals to create patterns from which the receiver can calculate its angles with respect to the transmitter. We design the curves in such a way that the angular information is most robust to errors caused by signal noise and motor jitters. Moreover, the shade is divided into multiple sectors, each providing independent positioning function, so as to maximize the position update rate. Experiments in various environments show that the system achieves 2-3 cm accuracy on average, with a 36 Hz update rate with a single transmitter. We present a product quality implementation of the system, and report the deployment experience in real-world environments, including autonomous driving and robotics navigation. CurveLight consistently offers centimeter-level accuracy and low latency, serving as a key component of the hybrid navigation solution for real systems in challenging scenarios.
{"title":"CurveLight: An Accurate and Practical Indoor Positioning System","authors":"S. Yan, Zhimeng Yin, Guang Tan","doi":"10.1145/3485730.3485934","DOIUrl":"https://doi.org/10.1145/3485730.3485934","url":null,"abstract":"This paper presents CurveLight, an accurate and practical light positioning system. In CurveLight, the signal transmitter includes an infrared LED, covered by a hemispherical and rotatable shade, and the receiver detects the light signals with a photosensitive diode. When the shade is rotating, the transmitter generates a unique sequence of light signals for each point in the covered space. The main novelty of the system design is a set of curves that define different regions, either transparent or translucent, on the shade. The regions allow the light signals to create patterns from which the receiver can calculate its angles with respect to the transmitter. We design the curves in such a way that the angular information is most robust to errors caused by signal noise and motor jitters. Moreover, the shade is divided into multiple sectors, each providing independent positioning function, so as to maximize the position update rate. Experiments in various environments show that the system achieves 2-3 cm accuracy on average, with a 36 Hz update rate with a single transmitter. We present a product quality implementation of the system, and report the deployment experience in real-world environments, including autonomous driving and robotics navigation. CurveLight consistently offers centimeter-level accuracy and low latency, serving as a key component of the hybrid navigation solution for real systems in challenging scenarios.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"240 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121094430","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}
Pei Tian, Fengxu Yang, Xiaoyuan Ma, C. Boano, Xin Tian, Ye Liu, Jianming Wei
Recently, several datasets shedding light on connectivity aspects in real-world LoRa networks have been provided to the community. However, they typically only involve a limited number of nodes, deal with unidirectional communication only, or focus on very specific physical layer settings. More importantly, existing datasets typically lack fine-grained environmental information such as the temperature in the surroundings of each node, which is known to have a strong impact on communication performance. In this work, we provide the community with a comprehensive dataset that fills all these gaps. We have collected detailed connectivity information in an outdoor LoRa network composed of 21 nodes for more than four months. Our dataset does not only focus on network-level performance (e.g., the average number of correctly-exchanged packets), but sheds light on link-level information such as the received signal strength, signal-to-noise ratio, and the number of available neighbours over time. We further collect environmental information from an online weather site, as well as the on-board temperature of each node in the network, which varies considerably across the deployed locations. We collect all this information while perpetually changing physical layer settings such as the spreading factor and the RF channel. A preliminary analysis of our dataset, which is available in Zenodo1, reveals that temperature has a significant correlation with the link quality and connectivity in the outdoor LoRa network, confirming the findings of earlier studies.
{"title":"Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network","authors":"Pei Tian, Fengxu Yang, Xiaoyuan Ma, C. Boano, Xin Tian, Ye Liu, Jianming Wei","doi":"10.1145/3485730.3493696","DOIUrl":"https://doi.org/10.1145/3485730.3493696","url":null,"abstract":"Recently, several datasets shedding light on connectivity aspects in real-world LoRa networks have been provided to the community. However, they typically only involve a limited number of nodes, deal with unidirectional communication only, or focus on very specific physical layer settings. More importantly, existing datasets typically lack fine-grained environmental information such as the temperature in the surroundings of each node, which is known to have a strong impact on communication performance. In this work, we provide the community with a comprehensive dataset that fills all these gaps. We have collected detailed connectivity information in an outdoor LoRa network composed of 21 nodes for more than four months. Our dataset does not only focus on network-level performance (e.g., the average number of correctly-exchanged packets), but sheds light on link-level information such as the received signal strength, signal-to-noise ratio, and the number of available neighbours over time. We further collect environmental information from an online weather site, as well as the on-board temperature of each node in the network, which varies considerably across the deployed locations. We collect all this information while perpetually changing physical layer settings such as the spreading factor and the RF channel. A preliminary analysis of our dataset, which is available in Zenodo1, reveals that temperature has a significant correlation with the link quality and connectivity in the outdoor LoRa network, confirming the findings of earlier studies.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121111422","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}
Xingda Chen, Deepak Ganesan, Jeremy Gummeson, Mohammad Rostami
Advances in flexible conductive substrates such as conductive wallpaper and paint present new opportunities for optimizing the performance of IoT nodes in smart homes and buildings. In this paper, we explore an unconventional use of such substrates for pulling frequencies of oscillators across IoT devices and wireless front-ends connected to the substrate. We show that by using this technique, we can replace precise crystal oscillators by lower precision and lower cost ceramic oscillators without compromising their ability to be used for tasks that require precise frequencies such as frequency-synchronized multi-static backscatter and synchronized sampling. We present an end-to-end design including a) analysis of conditions under which frequency pulling of oscillators across conductive substrates can work, b) a new technique to detect frequency locking across oscillators without requiring explicit communication, and c) an adaptive method that can be used to synchronize oscillators at minimum power consumption. We then show that these elements can be composed to design a high-performance multi-static backscatter system that performs as well as one that uses a shared high-precision clock but at an order of magnitude less monetary cost. We show that our system can scale and operate at very low power, while having low complexity since it requires no explicit interaction among devices attached to the substrate.
{"title":"COCOON: A Conductive Substrate-based Coupled Oscillator Network for Wireless Communication","authors":"Xingda Chen, Deepak Ganesan, Jeremy Gummeson, Mohammad Rostami","doi":"10.1145/3485730.3485940","DOIUrl":"https://doi.org/10.1145/3485730.3485940","url":null,"abstract":"Advances in flexible conductive substrates such as conductive wallpaper and paint present new opportunities for optimizing the performance of IoT nodes in smart homes and buildings. In this paper, we explore an unconventional use of such substrates for pulling frequencies of oscillators across IoT devices and wireless front-ends connected to the substrate. We show that by using this technique, we can replace precise crystal oscillators by lower precision and lower cost ceramic oscillators without compromising their ability to be used for tasks that require precise frequencies such as frequency-synchronized multi-static backscatter and synchronized sampling. We present an end-to-end design including a) analysis of conditions under which frequency pulling of oscillators across conductive substrates can work, b) a new technique to detect frequency locking across oscillators without requiring explicit communication, and c) an adaptive method that can be used to synchronize oscillators at minimum power consumption. We then show that these elements can be composed to design a high-performance multi-static backscatter system that performs as well as one that uses a shared high-precision clock but at an order of magnitude less monetary cost. We show that our system can scale and operate at very low power, while having low complexity since it requires no explicit interaction among devices attached to the substrate.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131643488","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}
Xinmin Fang, Xingyu Chen, Wenyao Xu, Zhengxiong Li
Virtual Reality (VR) training is an emerging method, which is widely deployed in more and more applications. Compared with traditional physical training and video games-based training, VR training can not only provide a sense of realism and immersion similar to physical training but can also train at any time and place, saving time and money. However, due to some constraints like lacking reflections of the ambient environment, the realism and immersion of VR training are insufficient. Therefore, in this paper, we propose enhanced VR training which senses the ambient environment and reflects them as dynamic unexpected training tasks to solve the above problems.
{"title":"Enhanced Virtual Reality: Exploring an Immersive and Realistic Virtual Reality Training for Nursing","authors":"Xinmin Fang, Xingyu Chen, Wenyao Xu, Zhengxiong Li","doi":"10.1145/3485730.3492870","DOIUrl":"https://doi.org/10.1145/3485730.3492870","url":null,"abstract":"Virtual Reality (VR) training is an emerging method, which is widely deployed in more and more applications. Compared with traditional physical training and video games-based training, VR training can not only provide a sense of realism and immersion similar to physical training but can also train at any time and place, saving time and money. However, due to some constraints like lacking reflections of the ambient environment, the realism and immersion of VR training are insufficient. Therefore, in this paper, we propose enhanced VR training which senses the ambient environment and reflects them as dynamic unexpected training tasks to solve the above problems.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133363387","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}
Deep fusion networks have received considerable attention lately due to the growing adoption of IoT devices, smartphones, and wearables that incorporate multiple sensing modalities, and their promising applications from human activity recognition to smart home automation. Despite recent advances in this area, there are several practical requirements that are often overlooked. Specifically, fusion networks must maintain their performance during momentary and long-term changes in the environment, be robust to sensor data quality issues, and have a reasonable size so that they can be deployed on resource-constrained devices. My PhD research aims to address these challenges by building robust multimodal fusion networks that rapidly generalize to new environments and have a smaller number of trainable weights, hence lower memory and carbon footprints.
{"title":"Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion","authors":"Sanju Xaviar","doi":"10.1145/3485730.3492897","DOIUrl":"https://doi.org/10.1145/3485730.3492897","url":null,"abstract":"Deep fusion networks have received considerable attention lately due to the growing adoption of IoT devices, smartphones, and wearables that incorporate multiple sensing modalities, and their promising applications from human activity recognition to smart home automation. Despite recent advances in this area, there are several practical requirements that are often overlooked. Specifically, fusion networks must maintain their performance during momentary and long-term changes in the environment, be robust to sensor data quality issues, and have a reasonable size so that they can be deployed on resource-constrained devices. My PhD research aims to address these challenges by building robust multimodal fusion networks that rapidly generalize to new environments and have a smaller number of trainable weights, hence lower memory and carbon footprints.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132379912","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}
Low-cost sensors are extensively used in numerous Internet of Things (IoT) applications to measure relevant physical processes. Today, processing context data is increasingly done by proprietary algorithms tuned to a specific use-case, e.g., a sensor measuring activity intensity of a cow. Readings from these sensors may be subject to data distribution shifts, which challenge robustness of models using these sensor readings. In this paper, we propose a new sensor data processing framework, which leverages a co-dependency between data quality and model robustness to detect performance issues of data-driven predictive models in the field. We show how distribution shifts in the input data impact the quality of the model, which relies on application-specific sensors, and present indicators capable of detecting such shifts in the wild. The proposed framework used in the context of precision cattle farming allows improving the quality of cow lameness predictive models on the field data by up to 62%.
{"title":"Exploring Co-dependency of IoT Data Quality and Model Robustness in Precision Cattle Farming","authors":"F. Papst, K. Schodl, O. Saukh","doi":"10.1145/3485730.3493447","DOIUrl":"https://doi.org/10.1145/3485730.3493447","url":null,"abstract":"Low-cost sensors are extensively used in numerous Internet of Things (IoT) applications to measure relevant physical processes. Today, processing context data is increasingly done by proprietary algorithms tuned to a specific use-case, e.g., a sensor measuring activity intensity of a cow. Readings from these sensors may be subject to data distribution shifts, which challenge robustness of models using these sensor readings. In this paper, we propose a new sensor data processing framework, which leverages a co-dependency between data quality and model robustness to detect performance issues of data-driven predictive models in the field. We show how distribution shifts in the input data impact the quality of the model, which relies on application-specific sensors, and present indicators capable of detecting such shifts in the wild. The proposed framework used in the context of precision cattle farming allows improving the quality of cow lameness predictive models on the field data by up to 62%.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133116843","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}
T. Maksymyuk, Marcel Volosin, J. Gazda, Madhusanka Liyanage
The paper presents a novel vision on the application of blockchain technology to empower the dynamic service provisioning in future 6G mobile networks. We propose a platform for decentralized service level agreement (SLA) negotiation between users and mobile network operators (MNOs) based on smart contracts and cryptocurrencies. In addition, the new quality of experience (QoE) model is proposed for end-users to customize their trade-off between SLA and service price. Finally, we develop the method of dynamic service selection among multiple MNOs that provides border-less connectivity for end-users with the guaranteed QoE regardless of the serving MNO.
{"title":"Blockchain-based Decentralized Service Provisioning in Local 6G Mobile Networks","authors":"T. Maksymyuk, Marcel Volosin, J. Gazda, Madhusanka Liyanage","doi":"10.1145/3485730.3493821","DOIUrl":"https://doi.org/10.1145/3485730.3493821","url":null,"abstract":"The paper presents a novel vision on the application of blockchain technology to empower the dynamic service provisioning in future 6G mobile networks. We propose a platform for decentralized service level agreement (SLA) negotiation between users and mobile network operators (MNOs) based on smart contracts and cryptocurrencies. In addition, the new quality of experience (QoE) model is proposed for end-users to customize their trade-off between SLA and service price. Finally, we develop the method of dynamic service selection among multiple MNOs that provides border-less connectivity for end-users with the guaranteed QoE regardless of the serving MNO.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133283917","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}
L. Gerrits, C. Samuel, Roland Kromes, F. Verdier, Severine Glock, P. Guitton-Ouhamou
Private or consortium blockchain networks have fewer verified participants and offer better throughput and transaction efficiency than public networks. However, as more and more blockchain consensuses are designed for private or consortium networks, their performances are often estimated without a practical use case implementation. In our use case, participants do not have to trust each other but still work together to build an ecosystem where users control their data and information. This paper analyzes the performance (transaction throughput, rejections, node participants) of Byzantine Fault Tolerant Consensus (BFT) using two blockchains: Hyperledger Sawtooth and Ethereum.
{"title":"Experimental Scalability Study of Consortium Blockchains with BFT Consensus for IoT Automotive Use Case","authors":"L. Gerrits, C. Samuel, Roland Kromes, F. Verdier, Severine Glock, P. Guitton-Ouhamou","doi":"10.1145/3485730.3493374","DOIUrl":"https://doi.org/10.1145/3485730.3493374","url":null,"abstract":"Private or consortium blockchain networks have fewer verified participants and offer better throughput and transaction efficiency than public networks. However, as more and more blockchain consensuses are designed for private or consortium networks, their performances are often estimated without a practical use case implementation. In our use case, participants do not have to trust each other but still work together to build an ecosystem where users control their data and information. This paper analyzes the performance (transaction throughput, rejections, node participants) of Byzantine Fault Tolerant Consensus (BFT) using two blockchains: Hyperledger Sawtooth and Ethereum.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114073816","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}
Tianyue Zheng, Zhe Chen, Shujie Zhang, Chao Cai, Jun Luo
Crucial for healthcare and biomedical applications, respiration monitoring often employs wearable sensors in practice, causing inconvenience due to their direct contact with human bodies. Therefore, researchers have been constantly searching for contact-free alternatives. Nonetheless, existing contact-free designs mostly require human subjects to remain static, largely confining their adoptions in everyday environments where body movements are inevitable. Fortunately, radio-frequency (RF) enabled contact-free sensing, though suffering motion interference inseparable by conventional filtering, may offer a potential to distill respiratory waveform with the help of deep learning. To realize this potential, we introduce MoRe-Fi to conduct fine-grained respiration monitoring under body movements. MoRe-Fi leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. The core of MoRe-Fi is a novel variational encoder-decoder network; it aims to single out the respiratory waveforms that are modulated by body movements in a non-linear manner. Our experiments with 12 subjects and 66-hour data demonstrate that MoRe-Fi accurately recovers respiratory waveform despite the interference caused by body movements. We also discuss potential applications of MoRe-Fi for pulmonary disease diagnoses.
{"title":"MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar","authors":"Tianyue Zheng, Zhe Chen, Shujie Zhang, Chao Cai, Jun Luo","doi":"10.1145/3485730.3485932","DOIUrl":"https://doi.org/10.1145/3485730.3485932","url":null,"abstract":"Crucial for healthcare and biomedical applications, respiration monitoring often employs wearable sensors in practice, causing inconvenience due to their direct contact with human bodies. Therefore, researchers have been constantly searching for contact-free alternatives. Nonetheless, existing contact-free designs mostly require human subjects to remain static, largely confining their adoptions in everyday environments where body movements are inevitable. Fortunately, radio-frequency (RF) enabled contact-free sensing, though suffering motion interference inseparable by conventional filtering, may offer a potential to distill respiratory waveform with the help of deep learning. To realize this potential, we introduce MoRe-Fi to conduct fine-grained respiration monitoring under body movements. MoRe-Fi leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. The core of MoRe-Fi is a novel variational encoder-decoder network; it aims to single out the respiratory waveforms that are modulated by body movements in a non-linear manner. Our experiments with 12 subjects and 66-hour data demonstrate that MoRe-Fi accurately recovers respiratory waveform despite the interference caused by body movements. We also discuss potential applications of MoRe-Fi for pulmonary disease diagnoses.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114133850","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}
D. Wójcik, T. Rymarczyk, E. Kozłowski, M. Gołąbek, M. Guzik
This research aimed to develop a high accuracy machine learning algorithm that can diagnose cardiovascular diseases from the stream of data from multiple body surface potential mapping devices equipped with 102 textile electrodes. The algorithm is based on the 1D convolutional neural network, trained on the comparable real-life data gathered from the FLUKE ECG simulator connected to the resistance-based human phantom. The developed neural network achieved an accuracy of 99.91% on the test data. Additionally, an additional algorithm was developed that can use the neural network to analyse the data streamed from the medical device and notice the medical staff about dangerous heart rhythms detected by the system.
{"title":"Ultrasound Tomography for Monitoring the Lower Urinary Tract","authors":"D. Wójcik, T. Rymarczyk, E. Kozłowski, M. Gołąbek, M. Guzik","doi":"10.1145/3485730.3492887","DOIUrl":"https://doi.org/10.1145/3485730.3492887","url":null,"abstract":"This research aimed to develop a high accuracy machine learning algorithm that can diagnose cardiovascular diseases from the stream of data from multiple body surface potential mapping devices equipped with 102 textile electrodes. The algorithm is based on the 1D convolutional neural network, trained on the comparable real-life data gathered from the FLUKE ECG simulator connected to the resistance-based human phantom. The developed neural network achieved an accuracy of 99.91% on the test data. Additionally, an additional algorithm was developed that can use the neural network to analyse the data streamed from the medical device and notice the medical staff about dangerous heart rhythms detected by the system.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"17 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430620","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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