Advances in Wearable Biosensors for Healthcare: Current Trends, Applications, and Future Perspectives

Abstract

Wearable biosensors are a fast-evolving topic at the intersection of healthcare, technology, and personalized medicine. These sensors, which are frequently integrated into clothes and accessories or directly applied to the skin, provide continuous, real-time monitoring of physiological and biochemical parameters such as heart rate, glucose levels, and hydration status. Recent breakthroughs in downsizing, materials science, and wireless communication have greatly improved the functionality, comfort, and accessibility of wearable biosensors. This review examines the present status of wearable biosensor technology, with an emphasis on advances in sensor design, fabrication techniques, and data analysis algorithms. We analyze diverse applications in clinical diagnostics, chronic illness management, and fitness tracking, emphasizing their capacity to transform health monitoring and facilitate early disease diagnosis. Additionally, this review seeks to shed light on the future of wearable biosensors in healthcare and wellness by summarizing existing trends and new advancements.

Keywords: bioanalysis; lifestyle; personalized health management; telemedicine; wearable biosensor.

Figure 1

Overview of the multimodal body sensors for BP estimation with IoT application. Copyright Elsevier (2024) [28].

Figure 2

Wearable sweat analysis patch based on SilkNCT, an integrated textile sensor patch for real-time and multiplex sweat analysis. (A,B) Schematic illustration of wearable sweat analysis patch mounted on human skin (A) and the multiplex electrochemical sensor array integrated in the patch (B). (C) Photograph of the wearable sweat analysis patch. (D,E) SEM (D) and TEM (E) images of the carbonized silk fabric, showing its hierarchical woven macrostructure and microcrystalline graphite-like microstructure, respectively. (F) High-resolution XPS spectrum of N1s for the carbonized silk fabric. a.u., arbitrary units. (G) EIS of the carbonized silk fabric prepared at different temperatures. Inset in (G) shows an equivalent circuit model. (H) Cyclic voltammograms of the carbonized silk fabric prepared at different temperatures in 0.1 M KCl solution containing 5.0 mM [Fe(CN)₆]^3−/4−. Copyright AAAS (2019) [53].

Figure 3

Schematics and images of the machine-learning-powered wearable sensor for distinguishable and predictable sensing. (A) Schematics of machine-learning-powered signal processing overcome the limitation of traditional signal processing, achieving accurate classification and quantification. CE, counter electrode; WE, working electrode; RE, reference electrode; ECA, electrochemical catalytic activity; KNN, k-nearest neighbor. (B) Schematic of micro-electrochemical system. (C) System-level block diagram showing the signal transduction, processing, and wireless transmission from the sensors to the user interface. ADC, analog-to-digital converter; DAC, digital-to-analog converter; I/O, input/output; BLE SoC, Bluetooth low energy system on chip. Copyright Elsevier (2024) [66].

Figure 4

(A) Circuit diagram for signal transmission of a POCT device; (B) The app installed on a smartphone for receiving and processing electrochemical signals; (C) Wearable device affixed to the skin surface; (D) Glucose catalyzed into glucuronic acid under the mediation of Co₃O₄/rGO/Pt; (E) Fluctuations in blood glucose and sweat glucose concentrations before and after meals; (F) Detection results of sweat glucose at different time intervals. Copyright Elsevier (2024) [98].

Figure 5

Performance and applications of the multifunctional biosensors. (a–c) The pH sensor’s performance, including stepped response, linear sensing, and sensing property under deformation and stretch. (d–f) The open-circuit potential responses to the respective analyte solutions of the Ca²⁺, Na⁺, and K⁺ sensors in their original state and under 60% strain. (g) Images of EMG and multifunctional sweat sensor testing. (h) Two-channel electromyography signals. (i) Multi-signal monitoring of human sweat from warm-up to running. Copyright Elsevier (2024) [109].

Figure 6

On-body sensing performance. (a) Main components of the patch. (b) Displaying the assembled patch, comparable in size to a 25-cent coin. (c) The integrated microneedle sensor is affixed to the upper arm of the wearer. (d) Comparison of ISF glucose concentration (mM) measured by microneedle sensors with reference measurements from commercial glucose blood strips. (e) Comparison of ISF lactate concentration (mM) measured by microneedle sensors with reference measurements from commercial lactate blood strips. (f) Comparison of ISF alcohol volume fraction measured by microneedle sensors with reference measurements from a commercial breathalyzer. Copyright Elsevier (2024) [110].

Figure 7

(a) Devices for measuring biomarkers using a sweat-based lactate biosensor, a Nova Biomedical blood lactate meter, and a fatigue questionnaire, the fatigue assessment scale (FAS), to obtain biomarkers from participants, including sweat lactate (SL) concentration, blood lactate (BL), and a subjective fatigue score. (b) Schematic drawing of the OECT sensor for measuring lactate concentration. (c) Comparisons of (a) sweat lactate, (b) blood lactate, and (c) FAS scale between experimental and control groups. Copyright Elsevier (2023) [139].

Figure 8

Accessory sensors such as (a) contact lenses and eyeglasses and (b) headbands have been used for real-time health monitoring, (c) an ELISA-based patch-type printed sensor for health sign monitoring, and (d) a versatile implantable sensor for real-time health monitoring, drug delivery, and data transmission, with versatile functions. Copyright Elsevier (2024) [154].