Recent advancements in machine learning enabled portable and wearable biosensors

Abstract

Recent advances in noninvasive portable and wearable biosensors have attracted significant attention due to their capability to offer continual physiological information for continuous healthcare monitoring through the collection of biological signals. To make the collected biological data understandable and improve the efficacy of these biosensors, scientists have integrated machine learning (ML) with biosensors to analyze large sensing data through various ML algorithms. In this article, we have highlighted the recent developments in ML-enabled noninvasive biosensors. Initially, we introduced and discussed the basic features of ML algorithms used in data processing to build an intelligent biosensor system and the capability to make clinical decisions. Next, the principles of portable and wearable biosensors, the application of different ML models in diverse biosensors for healthcare applications, and their impact on the performance of biosensors are discussed. The last section highlights the challenges (such as data privacy, consistency, stability, accuracy, scalable production, and adaptive learning capacity), future prospects, and necessary steps required to address these issues, spotlighting their revolutionizing impact on the healthcare industry for the development of next-generation ML-enabled efficient biosensors.

Keywords

• Machine learning
• Clinical decision making
• Portable and wearable biosensors