Emerging trends in amino acid detection: wearable devices and machine learning-assisted signal processing

Area of Science:

• Biochemistry and Analytical Chemistry
• Biomedical Engineering
• Data Science and Machine Learning

Background:

• Amino acids are vital metabolic biomarkers linked to diseases like cancer and cardiovascular conditions.
• Abnormal amino acid levels indicate various health issues, necessitating accurate monitoring.
• Recent technological progress enhances amino acid analysis for personalized healthcare.

Purpose of the Study:

• To review the latest advancements in amino acid analysis technologies.
• To focus on emerging trends in wearable sensing and machine learning applications.
• To outline challenges and future directions for intelligent health monitoring.

Main Methods:

• Review of flexible and wearable sensing devices for non-invasive amino acid monitoring in biofluids (blood, sweat, interstitial fluid).
• Analysis of machine learning (ML) approaches for processing complex signals, including linear regression, deep learning, SVMs, ensemble learning, and tree-based models.
• Examination of design principles, operational mechanisms, applications, and performance metrics of sensing devices and ML models.

Main Results:

• Development of wearable sensors enables continuous, non-invasive monitoring of amino acids.
• Machine learning models effectively interpret complex signals, mitigating interference and drift.
• Integration of these technologies promises advanced personalized health monitoring.

Conclusions:

• Wearable sensors and ML represent key trends in amino acid analysis.
• These advancements facilitate intelligent, integrated, and personalized health monitoring systems.
• Future research should focus on overcoming current challenges to further refine these systems.