Optimization of a Compact Wearable LoRa Patch Antenna for Vital Sign Monitoring in WBAN Medical Applications Using Machine Learning

被引:6
作者
Waly, Mohamed I. [1 ]
Smida, Jamel [2 ,3 ]
Bakouri, Mohsen [1 ]
Alresheedi, Bakheet Awad [1 ]
Alqahtani, Tariq Mohammed [1 ]
Alonzi, Khalid A. [4 ]
Smida, Amor [1 ,3 ]
机构
[1] Majmaah Univ, Coll Appl Med Sci, Dept Med Equipment Technol, Al Majmaah 11952, Saudi Arabia
[2] AlMaarefa Univ, Coll Appl Sci, Riyadh 11597, Saudi Arabia
[3] Univ Tunis El Manar, Fac Sci Tunis, Microwave Elect Res Lab, Tunis 1068, Tunisia
[4] Minist Def, Hlth Serv, Riyadh 12426, Saudi Arabia
关键词
LoRa; WBAN; RSSI; wearable; IoT; microstrip patch antenna; machine learning; optimization; IOT;
D O I
10.1109/ACCESS.2024.3434595
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study introduces an innovative and compact wearable Long-Range (LoRa) patch antenna developed for monitoring vital signs, with a focus on heart rate and body temperature, in medical applications of Wireless Body Area Networks (WBAN). The antenna functions within the 868 MHz and 915 MHz LoRa bands, filling a notable gap in current literature regarding compact, wearable antennas operating below 1 GHz. Fabricated on a Rogers Duroid RO3003 substrate, the antenna incorporates a U-slot on a conventional rectangular patch, a matching stub, and a partial ground plane to enhance impedance matching and performance efficiency. Furthermore, the antenna displays a bidirectional radiation pattern in the E-plane and an omnidirectional pattern in the H-plane at both frequencies, achieving a peak gain of 2.12 dBi and a radiation efficiency of 99.8% at 868 MHz. The antenna, measuring 80 x 60 mm(2) ( 0.23 lambda(0) x 0.17 lambda(0) ), was designed, simulated, and optimized using CST Microwave Studio (MWS) software. The performance under bending conditions was also assessed, revealing a bending an excellent efficiency with minimal impact on bandwidth and gain. Specific Absorption Rate (SAR) analysis indicated that all values were within the safety limits set by FCC and ICNIRP standards. Supervised regression machine learning models, specifically the ensemble regression model, were employed to predict resonance frequencies based on various antenna parameters, resulting in an R-squared score of 87.68%. This approach significantly reduced the computational time required for full-wave simulations, streamlining the design process. Real-world experimental validation involved open-field testing of the fabricated prototype for WBAN LoRa applications. The performance, evaluated on a LoRa transceiver system utilizing the LoRa SX1276, demonstrated the superior capabilities of the proposed antenna in heart rate and temperature monitoring, with an average RSSI improvement of -5 dBm at various points within a range of up to 1 km. This confirmed its improved signal transmission and reception capabilities in vital sign monitoring. The proposed antenna shows strong performance metrics and significant potential for WBAN in long-range applications, as evidenced by thorough experimental validations.
引用
收藏
页码:103860 / 103879
页数:20
相关论文
共 46 条
[1]   Machine Learning-Based Optimized 3G/LTE/5G Planar Wideband Antenna With Tri-Bands Filtering Notches [J].
Aliyu Babale, Suleiman ;
Kim Geok, Tan ;
Kamal Abdul Rahim, Sharul ;
Pao Liew, Chia ;
Musa, Umar ;
Fatihu Hamza, Mukhtar ;
Awadh Bakhuraisa, Yaser ;
Lim LL, Li Li .
IEEE ACCESS, 2024, 12 :80669-80686
[2]   Day-Ahead Electricity Price Forecasting Based on Hybrid Regression Model [J].
Alkawaz, Ali Najem ;
Abdellatif, Abdallah ;
Kanesan, Jeevan ;
Khairuddin, Anis Salwa Mohd ;
Gheni, Hassan Muwafaq .
IEEE ACCESS, 2022, 10 :108021-108033
[3]  
Alsager A., 2011, Design and Analysis of Microstrip Patch Antenna Arrays
[4]   Internet of Mobile Things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANs Standards and Supported Mobility [J].
Ayoub, Wael ;
Samhat, Abed Ellatif ;
Nouvel, Fabienne ;
Mroue, Mohamad ;
Prevotet, Jean-Christophe .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2019, 21 (02) :1561-1581
[5]   Low-Cost Sensor-Based and LoRaWAN Opportunities for Landslide Monitoring Systems on IoT Platform: A Review [J].
Bagwari, Swapnil ;
Gehlot, Anita ;
Singh, Rajesh ;
Priyadarshi, Neeraj ;
Khan, Baseem .
IEEE ACCESS, 2022, 10 :7107-7127
[6]   Developing a hybrid model of prediction and classification algorithms for building energy consumption [J].
Banihashemi, Saeed ;
Ding, Grace ;
Wang, Jack .
1ST INTERNATIONAL CONFERENCE ON ENERGY AND POWER, ICEP2016, 2017, 110 :371-376
[7]   Internet of Things (IoT): A Review of Its Enabling Technologies in Healthcare Applications, Standards Protocols, Security, and Market Opportunities [J].
Bhuiyan, Mohammad Nuruzzaman ;
Rahman, Md Mahbubur ;
Billah, Md Masum ;
Saha, Dipanita .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (13) :10474-10498
[8]   Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions [J].
Bin Zikria, Yousaf ;
Ali, Rashid ;
Afzal, Muhammad Khalil ;
Kim, Sung Won .
SENSORS, 2021, 21 (04) :1-7
[9]   Alternative Chirp Spread Spectrum Techniques for LPWANs [J].
Bizon Franco de Almeida, Ivo ;
Chafii, Marwa ;
Nimr, Ahmad ;
Fettweis, Gerhard .
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2021, 5 (04) :1846-1855
[10]   A Novel Design for Dual-Band Wearable Textile Eighth-Mode SIW Antennas [J].
Casula, Giovanni Andrea ;
Montisci, Giorgio ;
Muntoni, Giacomo .
IEEE ACCESS, 2023, 11 :11555-11569