AI-Enhanced Smart Textile Microwave Sensor for Real-time On-body Lactate Monitoring in Sports Applications
- Authors
- Sohail, Amir; Shah, Izaz Ali; Yoo, Hyoungsuk
- Issue Date
- Nov-2025
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Monitoring; Resonant frequency; Sensors; Biomedical monitoring; Permittivity; Microwave sensors; Microwave measurement; Biosensors; Glucose; Sensitivity; Artificial intelligence (AI); biosensors; deep learning (DL); machine learning (ML); microwave sensors; sweat lactate; textile sensors; wearable sensors
- Citation
- IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, v.74, pp 1 - 13
- Pages
- 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Volume
- 74
- Start Page
- 1
- End Page
- 13
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/210670
- DOI
- 10.1109/TIM.2025.3632454
- ISSN
- 0018-9456
1557-9662
- Abstract
- The growing demand for noninvasive, real-time physiological monitoring in sports and healthcare has propelled the development of advanced wearable biosensors. This study presents an artificial intelligence (AI)-powered smart textile microwave sensor designed for continuous on-body lactate monitoring in sweat, addressing the limitations of conventional electrochemical and microwave-based systems, particularly their limited linear detection ranges. This limitation hinders the ability to accurately capture the full spectrum of typical lactate levels in sweat. The proposed sensor employs a flexible, textile-based substrate integrated with a dual-port microwave configuration, enabling simultaneous monitoring of multiple microwave parameters. Analyzing these parameters using AI models enhances accuracy and sensitivity, and enables broad-range lactate monitoring. Experimental validation was conducted using a broad lactate solution concentration range (0 to 125 mM) and on-body trials during exercise sessions. The AI-driven analysis significantly improved predictive performance, reducing the root mean square error (RMSE) by 63.34% and the mean absolute error (MAE) by 54.84% compared to traditional single-parameter approaches. Furthermore, real-time sweat lactate monitoring on a volunteer demonstrated a strong correlation with a commercially available blood lactate analyzer, confirming the sensor’s practical reliability. This innovative textile-based biosensor, combining microwave sensing with AI analytics, offers a robust, noninvasive solution for continuous lactate monitoring, highlighting its potential for applications in sports performance optimization, diagnostics, and personalized health monitoring.
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