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ECG Monitoring System using IoT for Health Care applications

ECG Monitoring System using IoT for Health Care Applications

引用

Augmented Intelligence and Sustainable systems (ICAISS), International conference on

作者： K. Vijaipriya C. Priya Siji Sivanandan R. Krishnaswamy Department of Electronics and Communication Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Siddharth Institue of Engineering & Technology (SIETK) Puttur Andhra Pradesh India Department of Electronics and Communication Engineering Meenakshi Sundararajan Engineering College Chennai Tamil Nadu India Department of Electronics and Communication University College of Engineering Ariyalur Tamil Nadu India

The ECG monitoring system is an innovative method of heart health monitoring that makes use of the Internet of Things for healthcare purposes. With the use of Raspberry Pi, an electrocardiogram (ECG) sensor, and a cloud server, this system can monitor a patient's heart rate in real time and indefinitely, which has several benefits for both the patient and the doctor. Using the IoT, the ECG sensor can communicate to the cloud storage directly. The patient's electrical heart activity is monitored by use of an ECG sensor. After that, the sensor's output data is sent to the cloud server through Raspberry Pi, which operates as a connection between the sensor and the cloud server. Analyzing and evaluating ECG data in real time through cloud servers using advanced methods. The approaches are sensitive and accurate sufficient to detect cardiac irregularities such arrhythmias. When anomalies are detected, the system immediately generates realtime alerts to healthcare professionals. Instant alerts allow for prompt medical treatment, which improves patient outcomes and decreases the potential of catastrophic events. The cloud server provides a store for patient data and will enable doctors to access it from anywhere. In healthcare settings, this system's ability to identify cardiac problems early improves patient care and promotes preventative approaches to controlling heart health.

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Interfacing and Testing High-Resolution, Video-Based Avionics and Mission systems that Use ARINC 818-3

Interfacing and Testing High-Resolution, Video-Based Avionic...

引用

conference on Digital Avionics systems (DASC)

作者： Tim Keller Great River Technology Albuquerque NM USA

The complexity of modern mission and sensor systems continues to increase as the applications for video-based sensors become more demanding. Real-time, man-in-the-loop missions require higher levels of precision than previous class systems. This higher precision is achieved via higher-resolution, low-latency stereoscopic sensors and displays for better 3D performance. The new class of systems also sets the stage for removing the man-in-the-loop in favor of autonomous systems, for: aerial refueling; automatic targeting and tracking; and EO/IR ISR *** requirements of this new class of mission and sensor systems line up well with inherent features of the ARINC 818-3 (Avionics Digital Video Bus) protocol. Although some of these features have been defined in the specification for more than a decade, newer systems are just being developed that tap into some of the more advanced ARINC 818-3 features for the first time. For example:•64B/66B encoding using new control characters and ensuring interoperability between independently developed systems•Video concentration and de-concentration of multiple sensors onto a single link, while maintaining frame synchronous timing•Support for 4K+ stereo displays, including robustness testing capabilities and considerations for channel skew•Support for new pixel modes, including YCbCr and Bayer encoding.•Stringent Stereo Display timingThis paper provides lessons learned which highlight the tools and new test capabilities needed to develop and validate complex mission and video systems using multiple, high-speed EO/IR sensors and displays.

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Event-Driven and Scalable Digital Twin System for Real-Time Non-Destructive Testing in Industrial Computational systems

Event-Driven and Scalable Digital Twin System for Real-Time ...

引用

IEEE Annual conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Piotr Hajder Mateusz Mojżeszko Filip Hallo Lucyna Hajder Krzysztof Regulski Krzysztof Banaś Monika Pernach Danuta Szeliga Krzysztof Bzowski Kazimierz Michalik Adam Mrozek Łukasz Sztangret Marek Wilkus Tomasz Jażdżewski Wojciech Jędrysik Rafał Nadolski Andrzej Opaliński Łukasz Rauch AGH University of Krakow Kraków Poland

ISBN: (数字)9798331535537

ISBN: (纸本)9798331535544

This study presents a digital twin system designed to monitor, predict, and optimize linear welding parameters in real time, with applications in Industry 4.0 and smart manufacturing. The system leverages Kubernetes to seamlessly integrate various sensors, enabling their dynamic allocation to different aggregates and ensuring scalability for adding new devices as needed. The selected computational models, including machine learning and fuzzy logic-based approaches, provided effective real-time feedback to the welding aggregate by dynamically adjusting welding power. GitOps practices and event-driven communication using message queues facilitated efficient deployment and management in this dynamic and distributed environment, facilitating continuous system updates and minimizing downtime. By providing real-time feedback to welding machine operators and functioning as a digital twin, this system enhances both simulation capabilities and physical process control, demonstrating potential for cross-domain interoperability and advanced decision-making frameworks.

关键词： Adaptation models Welding Computational modeling Aggregates Scalability Real-time systems sensor systems Digital twins Fourth Industrial Revolution Testing

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Application of Deep Learning in Autonomous Mobile Robot Control: An Overview

Application of Deep Learning in Autonomous Mobile Robot Cont...

引用

Annual IEEE systems conference

作者： Minh Nguyen Rickey Dubay Department of Mechanical Engineering Intelligent Controls Laboratory University of New Brunswick Fredericton New Brunswick Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Autonomous mobile robots (AMRs) are reshaping industries by automating tasks across diverse sectors, including logistics, healthcare, agriculture, and manufacturing. Recent advancements in deep learning (DL) have enhanced traditional control systems, empowering AMRs to process high-dimensional sensor data, improve perception, and navigate complex, dynamic environments. These techniques enable AMRs to perform a wide array of sophisticated tasks, including real-time navigation in crowded and cluttered spaces, dynamic obstacle avoidance, and accurate object recognition in settings like warehouses and agricultural fields. Beyond industrial applications, AMRs are also making significant strides in healthcare, particularly in elderly care, where they provide personalized assistance, help with mobility, and monitor health metrics through advanced perception and control mechanisms. This paper provides an overview of DL techniques in AMR systems, examining the roles of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL). CNNs are explored for visual perception tasks such as object detection, scene understanding, and localization. RNNs are utilized for processing sequential and time series data, such as inertial measurement units or force/torque sensors, to enhance scene perception. Finally, RLs are applied to decision-making and path planning in uncertain and dynamic environments. The paper also addresses the growing role of DL in overcoming key challenges in AMR systems, including enhancing robustness to environmental variations, enabling scalability across diverse operational scenarios, and improving autonomous decision-making capabilities.

关键词： Deep learning Scalability Decision making Medical services Robot sensing systems Real-time systems Safety Mobile robots Older adults Autonomous robots

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RE-KGR: Relation-Enhanced Knowledge Graph Reasoning for Recommendation 26th

RE-KGR: Relation-Enhanced Knowledge Graph Reasoning for Reco...

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26th International conference on Database systems for advanced applications (DASFAA)

作者： He, Ming Zhang, Hanyu Wen, Han Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9783030731991;9783030732004

A knowledge graph (KG) has been widely adopted to improve recommendation performance. The multi-hop user-item connections in a KG can provide reasons for recommending an item to a user. However, existing methods do not effectively leverage the relations of entities and interpretable paths in a KG. To address this limitation, in this paper, we propose a novel recommendation framework called relation-enhanced knowledge graph reasoning for recommendation (RE-KGR) that combines recommendation and explainability by reasoning user-item interaction paths (UIIPs). First, instead of applying an alignment algorithm for preprocessing, RE-KGR directly learns the semantic representation of entities from structured knowledge by stacking relation-based convolutional layers to take full advantage of the KG. Moreover, RE-KGR infers user preferences by calculating the sum of all UIIPs between users and items. Finally, RE-KGR selects several UIIPs with the highest probabilities as possible reasons for the recommendations. Extensive experiments on three real-world datasets demonstrate that our proposed method significantly outperforms several state-of-the-art baselines and achieves superior performance and explainability.

关键词： Recommender systems Graph neural networks Knowledge graphs

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and Machine Learning 3

Detailed review on Integrated Healthcare Prediction System U...

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3rd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Parasar, Deepa Ali, Arif Pillai, N. Muthuvairavan Shahi, Archana Alfurhood, Badria Sulaiman Pant, Kumud Amity University Maharashtra Cse India Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India R.M.D Engineering College Computer Science and Business Systems Kavarapettai India Chitkara University Chitkara Business School India Princess Nourahbint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. sensors connected through wireless networks make it possible to track the actions of a whole population. sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology's function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

关键词： Forecasting

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Multi-object Multi-sensor Tracking Simulation Using Poisson Multi-Bernoulli Mixture Filter 23

Multi-object Multi-sensor Tracking Simulation Using Poisson ...

引用

23rd International conference on Digital Signal Processing and its applications, DSPA 2021

作者： Thanh, Le Ba Alexandrovich, Pogibelsky Dmitry Ruben, Paliyan Moscow Institute of Physics and Technology Department of Microprocessor Technologies in Intelligent Control Systems Dolgoprudniy Russia

ISBN: (纸本)9781728195513

Multi-object tracking and multi-sensor fusion algorithms are used in many areas of science and technology. For example, in an advanced field such as autonomous vehicles, multi-object tracking and multi-sensor fusion are indispensable technologies. The purpose of these technologies is to processing noisy measurements from different sensors to determine the number and state of objects with high accuracy. Some of the algorithms frequently used in multi-object tracking applications are Global Nearest Neighbor Filter (GNN), Joint Probabilistic Data Association Filter (JPDA), Multiple Hypothesis Tracking (MHT), Probability Hypothesis Density Filter (PHD), and other algorithms. These algorithms are very effective for estimating the state of objects and can combine measurements from multiple sensors to improve estimation accuracy [1,2]. But an even more powerful tool for multi-object tracking is the Poisson multi-Bernoulli mixture filter (PMBM Filter) [1]. In this paper, we will introduce the PMBM filter and simulate an algorithm using a PMBM filter that combines measurements from multiple automotive sensors to estimates object's states. At the same time, we also compare the performance of the aforementioned algorithms in some specific simulation scenarios. © 2021 IEEE.

关键词： Mixtures

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Deterministic Multi-sensor Measurement-adaptive Birth using Labeled Random Finite Sets

Deterministic Multi-sensor Measurement-adaptive Birth using ...

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International conference on Information Fusion

作者： Jennifer Bondarchuk Anthony Trezza Donald J. Bucci Advanced Technology Laboratories Lockheed Martin Cherry Hill NJ USA

Measurement-adaptive track initiation remains a critical design requirement of many practical multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A truncation procedure has also been provided that leverages a stochastic Gibbs sampler to truncate the birth density for scalability. In this work, we introduce a deterministic herded Gibbs sampling truncation solution for efficient multi-sensor adaptive track initialization. Removing the stochastic behavior of the track initialization procedure without impacting average tracking performance enables a more robust tracking solution more suitable for safety-critical applications. Simulation results for linear sensing scenarios are provided to verify performance.

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TacLink-Integrated Robot Arm toward Safe Human-Robot Interaction

TacLink-Integrated Robot Arm toward Safe Human-Robot Interac...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Quan Khanh Luu Alessandro Albini Perla Maiolino Van Anh Ho Japan Advanced Institute of Science and Technology (JAIST) Ishikawa Japan Oxford Robotics Institute University of Oxford Oxford England Japan Science and Technology Agency (JST) PRESTO Kawaguchi Saitama Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Recent developments in vision-based tactile sensing offer a simple means to enable robots to perceive touch interactions. However, existing sensors are primarily designed for small-scale applications like robotic hands, lacking research on their integration for large-sized robot bodies that can be leveraged for safe human-robot interactions. This paper explores the utilization of the previously-developed vision-based tactile sensing link (called TacLink) with soft skin as a safety control mechanism, which can serve as an alternative to conventional rigid robot links and impact observers. We characterize the behavior of a robot integrated with the soft TacLink in response to collisions, particularly employing a reactive control strategy. The controller is primarily driven by tactile force information acquired from the soft TacLink sensor through a data-driven sim2real learning method. Compared with a standard rigid link, the results obtained from collision experiments also confirm the advantages of our "soft" solution in impact resilience and in facilitating controls that are difficult to achieve with a stiff robot body. This study can act as a benchmark for assessing the efficiency of soft tactile-sensitive skins in reactive collision responses and open new safety standards for soft skin-based collaborative robots in human-robot interaction scenarios.

关键词： Human-robot interaction sensor phenomena and characterization Observers Robot sensing systems Skin Safety Collision avoidance Robots Standards Resilience

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Photovoltaic abnormal data cleaning based on fuzzy clustering-quartile algorithm

Photovoltaic abnormal data cleaning based on fuzzy clusterin...

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Industrial Cyber-Physical systems (ICPS)

作者： Yidong Li Danyun Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

The irradiance-power curve is an important basis for examining the operating status of photovoltaic power stations. In the actual operation process, sensor failure, abnormal communication and equipment damage will bring a large number of abnormal values to the output data of photovoltaic power plants. It will have a significant impact on a variety of applications based on photovoltaic output data. This paper analyzes the typical outliers on the irradiance-power curve and proposes a photovoltaic output data cleaning method based on fuzzy clustering algorithm and quartile algorithm. By comparing with the quartile method, it is proved that this method can effectively identify abnormal data when there are a large number of outliers in the photovoltaic output data.

关键词： Photovoltaic systems Training Clustering algorithms Prediction algorithms Cleaning Filling Data models

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