Optical sensor based quantum computing in sports medicine for diagnosis and data analysis using machine learning model

Xu, Wanghao

doi:10.1007/s11082-023-06066-z

Cited by 1 publication

(2 citation statements)

References 10 publications

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“…Sensor design has evolved to include features that provide greater precision in operation [3]. Bioelectrical, biophysical, and biochemical signals can be employed in various contexts, including sports development, medical analysis and prevention, as well as rehabilitation [4]. Sensors play a crucial role in risk prevention, including health, road, natural, and architectural risks [5].…”

Section: Introductionmentioning

confidence: 99%

“…A recent study proposed a novel method for assessing the health status of athletes in sports medicine using optical sensors and quantum computing. The data collected from optical sensors is analyzed using a recurrent neural network (ResNet) adapted for quantum convolutional learning (NResNetRNN_QCL) [4]. In this additional study, a machine learning (ML) approach is used to analyze musical signals in a health monitoring system, in conjunction with an optical sensor.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%