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Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060509.

Design and Implementation of Portable Physical Health Indicators Based on FPGA

Author(s)

Yuan Cao

Corresponding Author:
Yuan Cao
Affiliation(s)

School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China

Abstract

Physical health indicators are developing towards miniaturization and diversified functions, and require them to have certain real-time analysis and diagnostic functions. To achieve such a function, it is necessary for the processor to have high performance and powerful algorithm processing capabilities. FPGA is now the ideal choice among mainstream processors and programmable chips. This article first introduces the portable and recognition system framework of FPGA, and then improves the application of signal processing to beneficial detection and diagnosis of various indicators of physical health. Then, in the design of physical health indicators using FPGA, five commonly used standard indicators are introduced first, and then five indicators of physical health are implemented using FPGA. The pressure sensor MS5536 is used to obtain the sampling data of the recognition system. Process the data and ensure that the collected data is valid. At the end of this article, DSP, ARM, and microcontroller are introduced as processors to complete data calculation and control functions, which cannot be well completed. However, various functions of physical health indicators can be achieved in FPGA, and then FPGA chips and signal processing are used the successful resolution rate of the event is higher than that of the other two systems, and the experimental results show that the lowest probability can reach 80.2%.

Keywords

Physical Health Indicators; Portable devices with FPGA; Identification System; Signal Processing

Cite This Paper

Yuan Cao. Design and Implementation of Portable Physical Health Indicators Based on FPGA. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 68-74. https://doi.org/10.25236/AJCIS.2023.060509.

References

[1] Saadeh W, Khan F H, Altaf M A B. Design and implementation of a machine learning based EEG processor for accurate estimation of depth of anesthesia[J]. IEEE transactions on biomedical circuits and systems, 2019, 13(4): 658-669.

[2] Bárcenas A R, Herrera R P, Calero J A M, et al. Optimized Design and Implementation of Digital Lock-In for Planetary Exploration Sensors [J]. IEEE Sensors Journal, 2022, 22(23): 23367-23379.

[3] Zhu B, Wang Z, Zhang J, et al. Design and implementation of a portable high-performance gamma-ray camera[J]. IEEE Transactions on Nuclear Science, 2021, 68(6): 1309-1318.

[4] Wang X, Liang H, Wang Y, et al. High-throughput portable true random number generator based on jitter-latch structure [J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, 68(2): 741-750.

[5] Sujaya B L, Bhanu Prashanth S B. An efficient hardware-based human body communication transceiver architecture for WBAN applications [J]. Global Transitions Proceedings, 2021, 2(2): 152-156.

[6] Fang Z, Yang C, Zheng Z, et al. A mixed-signal chip-based configurable coherent photoacoustic-radar sensing platform for in vivo temperature monitoring and vital signs detection [J]. IEEE Transactions on Biomedical Circuits and Systems, 2021, 15(4): 666-678.

[7] Shahadi H I, Kadhim M K, Almeyali N M, et al. Design and Implementation of a Smart, Interactive, and Portable System for Monitoring of Human Vital Signs[J]. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 2021, 7(1): 30-42.

[8] Shi P, Li Y, Zhang W, et al. Design and Implementation of Bionic MEMS Electronic Heart Sound Stethoscope [J]. IEEE Sensors Journal, 2021, 22(2): 1163-1172.

[9] Zhang Y. Research on multi-channel data acquisition system of production index information based on genetic algorithm [J]. International Journal of Information and Communication Technology, 2022, 20(3): 245-257.

[10] Marini F, Bellato M, Bergnoli A, et al. FPGA Implementation of an NCO Based CDR for the JUNO Front-End Electronics[J]. IEEE Transactions on Nuclear Science, 2021, 68(8): 1952-1960.

[11] Almomany A, Ayyad W R, Jarrah A. Optimized implementation of an improved KNN classification algorithm using Intel FPGA platform: Covid-19 case study [J]. Journal of King Saud University- Computer and Information Sciences, 2022, 34(6): 3815-3827.

[12] Gómez-García C A, Askar-Rodriguez M, Velasco-Medina J. Platform for healthcare promotion and cardiovascular disease prevention[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(7): 2758-2767.

[13] Zheng X, Xu C, Hu X, et al. The software/hardware co-design and implementation of SM2/3/4 encryption/decryption and digital signature system [J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2019, 39(10): 2055-2066.

[14] Della Sala R, Bellizia D, Scotti G. A novel ultra-compact FPGA-compatible TRNG architecture exploiting latched ring oscillators [J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 69(3): 1672-1676.

[15] Sun B, Yang S, Yu C. Research on Multi-Parameter Portable Water Quality Detection System Based on ZYNQ Image Processing Technology [J]. Polish Journal of Environmental Studies, 2023, 32(2): 1353-1370.