## 3 Measurements
### Measurements with the Photonic/Wireless Compound System  
Figure 9 shows the measured data of two full gait cycles for a walking speed of 2 km/h with both the FBG (dashed line) and the sensing modules (solid line) from the seven subjects. These charts present both the measured angles between the two inertial sensors and the wavelength variation measured with the FBG for two complete gait cycles, so the correlation between these two measurands is easily and rapidly achieved. Each one of the waveforms is classified with the corresponding subject's identification letter. The waveforms for the two measuring systems were filtered using pre-existing functions from Matlab which implements a classical method of a windowed linear-phase FIR (finite impulse response) digital filter design. The performance of the wireless modules in the proposed gait monitoring system was validated through experimental tests involving seven volunteers walking on a treadmill under controlled conditions. Each module, operating in the 2.4 GHz frequency band, maintained stable and uninterrupted communication throughout the tests. Importantly, the presence of ambient radio frequency interference such as Wi-Fi, Bluetooth, and other common wireless communication sources did not compromise data integrity or transmission quality. This resilience against external radio noise demonstrates the robustness of the system's custom MAC protocol (eLPRT), which ensures reliable packet delivery even in electromagnetically noisy environments. The wireless modules consistently transmitted synchronized sensor data to the base station without noticeable delays or packet loss, confirming the system's capability for accurate, real-time monitoring in typical indoor settings with potential RF interference. The configuration was based on a standard high-pass filter with a cutoff frequency of 15 Hz in order to remove possible inherent noise in the system. Each one of the individual gaits is identified by the different angles achieved by the two sections of the leg where the inertial sensors are placed on and the walking frequencies. Since the speed is the same for every volunteer, each one of them naturally adapts their walking style to that speed. By performing the test at the same speed, the variables that describe each individual gait are narrowed down. This means that the natural differences that occur in the walking process between, for example, subjects C and E are better observed. When the subjects were walking with no imposed constraints, considerable differences in the chosen walking speeds were detected, in agreement with previous work [45]. So, setting the same speed for every subject allows for the characterization of all the gait cycles with just a single reference [18]. Figure 9 illustrates the wavelength shift signals captured by the Fiber Bragg Grating (FBG) sensor for each of the seven volunteer subjects during treadmill walking at a controlled speed of 4 km/h. Each curve corresponds to an individual subject and represents the strain fluctuations over time as the knee undergoes flexion and extension throughout the gait cycle. The waveform patterns reveal the cyclical nature of knee joint motion, characterized by periodic peaks and troughs that align with the stance and swing phases of the gait cycle. Notably, although the fundamental shape of the waveform is consistent across all subjects--demonstrating the repeatability and reliability of the FBG signal--variations in amplitude and waveform sharpness are evident. These differences can be attributed to physiological variability among the participants, including leg morphology, walking style, and muscle dynamics. Subjects with greater muscle mass or larger knee circumferences tend to exhibit higher strain magnitudes due to increased deformation during knee movement. The signal patterns also confirm the high sensitivity of the FBG sensor to dynamic mechanical changes, with a clear temporal resolution that allows for the identification of gait phases within a single step cycle.