Ultra-High-Frequency Radio-Frequency-Identification Baseband Processor Design for Bio-Signal Acquisition and Wireless Transmission in Healthcare System

Lee, Shuenn-Yuh; Tsou, Chieh; Huang, Peng-Wei

doi:10.1109/tce.2019.2956627

Cited by 21 publications

(7 citation statements)

References 23 publications

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“…Smart acquisition of biosignals is an important aspect of smart healthcare and the Internet of Medical Things (IoMT). An Application Specific Integrated Circuit (ASIC)-based wireless biosignal acquisition consumer electronic device using Radio Frequency Identification (RFID) at an Ultra High Frequency (UHF) has been presented in [7]. A different approach involving the use of a smartwatch with local differential privacy to collect health data was proposed in [8].…”

Section: Related Work In Consumer Electronics For Smart Healthcare a Existing Consumer Electronics For Smart Healthcarementioning

confidence: 99%

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EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals

Olokodana

Mohanty

Kougianos

et al. 2021

IEEE Trans. Consumer Electron.

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Epileptic seizures present a serious danger to the lives of their victims, rendering them unconscious, lacking control, and may even result in death only a few seconds after onset. This gives rise to a crucial need for an effective seizure detection method that is fast, accurate, and has the potential for mass market adoption. Kriging methods have a good reputation for high accuracy in spatial prediction, hence, their extensive use in geostatistics. This paper demonstrates the successful application of Kriging methods for an effective seizure detection device in an edge computing environment by modeling the brain as a spatial panorama. We hereby propose a novel wearable for real-time automated seizure detection from EEG signals using three different types of Kriging, namely, Simple Kriging, Ordinary Kriging and Universal Kriging. After multiple experiments with electroencephalogram (EEG) signals obtained from seizure patients as well as those from their healthy counterparts, the results reveal that the three Kriging methods performed very well in accuracy, sensitivity and latency of detection. It was found however, that Simple Kriging outperforms the other Kriging methods with a mean seizure detection latency of 0.81 sec, a perfect specificity, an accuracy of 97.50% and a sensitivity of 94.74%. The results in this paper compare well with other seizure detection models in the literature but their excellent seizure detection latency surpasses the performance of most existing works in seizure detection.

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Section: Related Work In Consumer Electronics For Smart Healthcare a Existing Consumer Electronics For Smart Healthcarementioning

confidence: 99%

“…Signal Acquisition [7], [8] Design of an application specific integrated circuit (ASIC) with radio frequency identification (RFID) at an Ultra High Frequency (UHF) or the use of smartwatch in an enhanced privacy environment achieved with local differential privacy (LDP).…”

Section: Categorymentioning

confidence: 99%

EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals

Olokodana

Mohanty

Kougianos

et al. 2021

IEEE Trans. Consumer Electron.

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“…With the rapid development of network technology in recent years, the network of community care has become a trend [6]. Using the Internet for community medical care can on the one hand strengthen information sharing with large medical institutions such as hospitals [7][8][9]; the network monitoring system generally adopts the form of server/client [10]. The client mainly carries out the collection and preliminary processing of physiological information; the server mainly carries out data collection and subsequent processing; the client and the server carry out data exchange through the network [11].…”

Section: Introductionmentioning

confidence: 99%

Design of Health Detection System for Elderly Smart Watch Based on Biosignal Acquisition

Zhu¹,

Wang

Wang³

2022

Journal of Sensors

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The health monitoring of the elderly has attracted increasing attention of researchers. Based on the biosignal acquisition method, this paper proposes a design structure of the health detection system for smart watches for the elderly and realizes the effective detection of health signals by analyzing the Lipschitz exponent of the maximum value column of the transform. The multiphysiological parameter acquisition and monitoring system of the wearable smart watch designed in this paper can continuously monitor the physiological parameters of the elderly such as body temperature, pulse, and respiration for a long time and solve the problem of the accuracy of the health detection of the elderly. In the simulation process, based on the performance of the synchronization source and the difference of the network path, the model applies the multivariate and multiscale biological signals to collect the human gait acceleration. The experimental results show that, compared with the international recognition rate obtained for this data set, the highest recognition rate obtained by the method in this paper reaches 96.5%, which can provide a calibration accuracy of 1 ~ 50 ms, and the synchronized system time and the national time service center network are given. The error obtained by comparing the published time is within 50 ms, which meets the accuracy requirements of the time protocol. The results fully prove that the algorithm in this paper can effectively extract the biosignal features of the elderly’s health detection and has good statistical features and classification accuracy.

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“…In recent years, the related technologies of wireless image transmission system have developed rapidly, but there are still some limitations, such as limited transmission bandwidth, inflexible transmission mode, and communication and image transmission quality assurance mechanism. Therefore, the research on these related aspects has extremely important social and economic value [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

Zhi-jun

Liu

2021

Wireless Communications and Mobile Computing

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In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time-domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP-CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.

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Ultra-High-Frequency Radio-Frequency-Identification Baseband Processor Design for Bio-Signal Acquisition and Wireless Transmission in Healthcare System

Cited by 21 publications

References 23 publications

EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals

EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals

Design of Health Detection System for Elderly Smart Watch Based on Biosignal Acquisition

Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

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