An AED-UAV Healthcare Information Robot Integration System Based on Edge Computing Airport Station

Su, Weibin; Delima, Allemar Jhone P.; Machica, Ivy Kim D.; Arroyo, Jan Carlo T.; Pan, Xiangbin

doi:10.1145/3556267.3556270

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Through qualitative and quantitative research, it enhances the accuracy of grey prediction, improving early warning capabilities for first aid flight safety risks. This provides a reliable method and theory for hospital emergency command and control centers dealing with UAV flight safety [8].…”

Section: Related Workmentioning

confidence: 99%

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Pan,

Wang,

et al. 2024

IEEE Access

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Ensuring flight safety for unmanned aerial vehicles (UAVs) is a critical concern, necessitating effective mathematical modeling for safety forecasting in both academic and industrial contexts. This study addresses this need by combining the capabilities of deep neural networks and grey analysis to create a comprehensive mathematical modeling approach focused on spatial information service (SIS). The paper introduces a novel spatial information-aware flight security forecasting model for UAVs, emphasizing the transformative impact of the new methodology. Traditionally, factors influencing flight safety are identified and formulated based on SIS chain technology, SIS management rules, SIS business processes, and spatial SIS chain verification. To address the challenges posed by significant data volatility and missing data in the sample dateset, a non-equally spaced GM (1,1) model with an approximate nonsimultaneous exponential law series is developed for prediction. Subsequently, multiple influencing factors are encoded and input into a specific BP neural network structure. The paper concludes with simulation experiments to evaluate the proposed model. The results of the simulation analysis demonstrate that the integration of deep learning and grey analysis in the proposed model effectively recognizes flight security risks with high efficiency. This underscores the transformative potential of the new approach in enhancing UAV flight safety forecasting.

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Section: Related Workmentioning

confidence: 99%

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Pan,

Wang,

et al. 2024

IEEE Access

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A Survey About 5G-Based Robotics: Implementations, Possibilities and Limitations

Portenschlager,

Pache,

Kranzer

2023

2023 IEEE 9th World Forum on Internet of Things (WF-IoT)

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Smart IoMT Framework for Supporting UAV Systems with AI

et al. 2022

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The health monitoring system is one of the most innovative technologies that has gained traction in the Internet of Medical Things (IoMT). It allows the connection of multiple sensors and actuators that can capture and monitor the data through the web page or mobile application. IoMT technology not only provides communications but also will provide monitoring, recording, storage, and display. IoMT in healthcare is used for measuring the vital signs of the human body, which allows medical professionals to assess the well-being of a patient. The doctor may recommend lifestyle modifications, prescribe more tests, or diagnose a disorder according to the results. This paper illustrates the remote-control health monitoring system (HMS) with the integration of a UAV, which allows the doctor to access the data and analyze the patient data remotely. Thus, the proposed HMS-UAV system aims to measure the temperature, humidity, blood pressure, heart rate, and SpO2 and stores the data on the UAV. Several sensors were thus used namely DHT11, MAX30102, Myoware and K24C16, and the Raspberry Pi camera. Reduced hospital stays and avoidance of readmissions are benefits of remote patient monitoring with IoMT-based UAVs. Contrary to its advantages, IoMT has flaws in information processing since a huge volume of data are needed to be handled in a single environment. One major novel inclusion in this work is to measure multiple parameters and provide a comparative analysis for all of them. Furthermore, the functionality of video recorded and stored is included where the doctor can surveil the patient.

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An AED-UAV Healthcare Information Robot Integration System Based on Edge Computing Airport Station

Cited by 3 publications

References 25 publications

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

A Survey About 5G-Based Robotics: Implementations, Possibilities and Limitations

Smart IoMT Framework for Supporting UAV Systems with AI

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