# Non-Invasive Glucose Measurement Technique Based on Time-of-Flight
## 2 Operating Principle  
In this paper, a novel sensor for the measurement of glucose in an aqueous solution is presented. The sensor created uses off-the-shelf parts and achieves detection of changes of glucose concentration, in a physiological saline sample. The proposed sensor is based on the measurement of the change of ToF when light travels through a sample containing glucose. ToF sensors are widely utilized in distance measurement due to their precision and versatility. The fundamental principle involves emitting a signal that travels to a target and reflects back to the sensor. The elapsed time, known as the time of flight, is directly related to the distance. While direct ToF measurement uses the travel time of the signal, phase-based ToF sensors utilize the phase difference between transmitted and received signals to infer distance, offering enhanced accuracy, particularly in short-range and high-precision applications. In phase-based ToF sensors, such as the one used here, the transmitted signal is typically a continuous sinusoidal wave:  
\[S(t)=A\sin(2\pi ft) \tag{1}\]  
When this wave reflects off a target and is received back, it exhibits a phase shift:  
\[R(t)=A\sin(2\pi ft+\phi) \tag{2}\]  
The phase shift \(\phi\) is directly proportional to the propagation delay (\(\Delta t\)) of the signal:  
\[\Delta t=\frac{\phi}{2\pi f} \tag{3}\]  
Using this time delay, the distance \(d\) can be computed as follows:  
\[d=\frac{c\cdot\Delta t}{2}=\frac{c\cdot\phi}{4\pi f} \tag{4}\]  
Here, \(c\) represents the speed of the wave (e.g., speed of light for optical signals). This method is advantageous for achieving high precision as small changes in phase can be detected with sub-wavelength accuracy. Phase-based ToF sensors offer high accuracy due to their sensitivity to small phase shifts, making them ideal for applications requiring fine resolution. Additionally, such sensors allow for high sampling rates, as they do not require measurement of a full signal round-trip time, enabling real-time applications. However, phase difference measurement also presents challenges. If the round-trip distance exceeds the signal wavelength, the phase shift may repeat, leading to ambiguity. This issue can be addressed using multiple modulation frequencies to distinguish between different phase cycles. A second challenge is their environmental sensitivity--optical ToF sensors can be affected by the presence of ambient light. This can be countered either with selective filters or with precise setups that decrease ambient light effects. A relatively simple technique for this would be a heterodyne receiver as the one shown in Figure 1. (Additional information on ToF can be found in the work of [PERSON] et al. (2017)). For the sensor presented here, modulated, non-polarized light of a narrow wavelength centered at 850 nm enters the sample where it is absorbed and scattered by the presence of glucose (Figure 2). The optical properties of the tissue, including its scattering and absorption coefficients, are sensitive to changes in glucose concentration, making this a promising approach for non-invasive glucose monitoring. [PERSON] et al. demonstrated that glucose concentration significantly affects the velocity of light and the scattering coefficient in tissue-simulating phantoms, providing a critical understanding of how glucose alters light transport properties in biological tissues [8]. This behavior directly impacts the intensity of light reaching the sensor due to the increased frequency of scattering events. Consequently, these scattering events influence the accuracy and sensitivity of the measurements taken by the developed sensor. Changes in blood glucose levels also affect the extracellular fluid (ECF), leading to modifications in the optical properties of the tissue. [PERSON] et al. observed that the reduced scattering coefficient can be utilized to detect glucose level changes both in the ECF and the bloodstream, supporting the use of scattering-based optical techniques for glucose monitoring [9].