# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 4 Non-Invasive Methods
### Infrared Absorption Spectroscopy
introduced a microwave resonator-based sensor designed for continuous glucose monitoring from the interstitial fluid (ISF). This sensor features a substrateless split-ring resonator tag, which is electromagnetically coupled with a reader. The sensing element does not require power consumption; thus, microwave resonator-based glucose sensors hold significant potential for integration into the artificial pancreatic systems [108]. Apart from these, several studies have explored resonance-based sensors for glucose measurement in the microwave spectrum. For instance, one study demonstrated that a micro-resonator utilizing a metal-insulator-semiconductor structure could serve as a reliable indicator of glucose levels [109]. Another study reported a highly sensitive resonator-based microwave biosensor for real-time blood glucose detection [110]. Additionally, radiofrequency-based biosensors have been investigated for real-time, continuous glucose monitoring [111]. Building on these advancements, researchers have developed a soft, smart contact lens that integrates glucose sensors and wireless power transfer circuits, and display pixels for the real-time visualization of glucose levels [112]. The lens developed by [PERSON] and his team features a hybrid structure combining transparent, stress-tunable materials, with mechanically reinforced islands to house electronic devices and elastic joints for stretchable components like antennas and interconnects (Figure 12a) [112]. The hybrid materials are chosen for their minimal refractive index difference, ensuring high transparency (93%) and low haze (1.6%) to maintain optical clarity. The elastic portion maximizes oxygen permeability for eye health. Stretchable and transparent electrodes made of one-dimensional ultralong metal nanofibers (mNFs). In this smart contact lens, the antenna receives radiofrequency (RF) signals from a transmitter (with a transmission distance of less than 9 mm), and the rectifier, which consists of a diode and capacitor, converts the AC signals into DC to power the LED and operate the glucose sensor (Figure 12b) [112]. When the glucose concentration in tear fluid exceeds the threshold, the sensor's resistance decreases. This reduction lowers the overall resistance of the parallel circuit that includes the LED and sensor, while the resistance of other components, such as the antenna and rectifier, remains unchanged. As a result, the bias applied to the parallel circuit of the LED and sensor decreases, which, under a constant voltage, can turn the LED on or off based on the glucose level (Figure 12c). Thus, the glucose concentration in tear fluid is detected by a graphene-based sensor, and the data are wirelessly transmitted to a display system that uses LEDs to show real-time glucose levels. This non-invasive system enables glucose monitoring without bulky external equipment. In vivo testing on rabbits confirmed the lens's safety and reliability. This smart contact lens platform offers potential for continuous health monitoring and applications in drug delivery and augmented reality. However, challenges such as similar signal-to-noise ratios, interference from nearby radiofrequency devices, and difficulties in distinguishing glucose from other biological components underscore the need for improved sensitivity, accuracy, and stability in these sensors. One potential solution lies in the integration of artificial intelligence (AI) and machine learning algorithms. For example, [PERSON] et al. conducted a pilot study of a non-invasive, wrist-worn device that analyzes resonance shifts in the microwave spectrum using AI. This device features a dial resonating sensor (DRS) to measure bulk plasma glucose levels, which are then converted into glucose readings. The device was able to make accurate predictions of plasma glucose levels across four cohorts, achieving an average mean absolute relative difference of 10.3% through a random sampling method applied to the complete dataset [113].