## 1 Introduction  
Monitoring body kinematics in humans is a growing area of engineering applied to medicine, with universities, high-performance sports centers, and healthcare institutions developing methods to accurately measure and evaluate human body movement for various purposes [1; 2]. The primary objectives for monitoring human body kinematics are to improve athletic performance [3; 4; 5] in competitions and to evaluate the efficacy of prescribed therapies and rehabilitation based on information provided by measuring limb movements [6; 7; 8; 9; 10]. These efforts have gained traction as musculoskeletal disorders and mobility impairments have become leading causes of disability, making continuous and accurate movement monitoring essential in modern healthcare. Several monitoring systems have been developed, including electronic systems using wearable wireless sensor networks [11; 12], motion capture techniques [13; 14], and advanced software algorithms for signal processing and motion classification [15; 16]. These systems, while effective, often present limitations in terms of cost, operational complexity, or the requirement for controlled environments, such as motion capture labs. For instance, optical tracking systems, though highly accurate, rely heavily on camera infrastructure and suffer from occlusion issues and limited portability [17; 18]. Wearable inertial measurement units (IMUs) offer a more mobile alternative but are often challenged by cumulative drift and magnetic interference, especially in real-world, unsupervised conditions [19; 20; 21; 22; 23]. Other applications for limb posture monitoring include assessing certain neurological and orthopedic diseases [19; 20; 21; 22; 23], monitoring human biomechanical parameters during rehabilitation [24], and analyzing joint kinematics for fall risk prediction and movement optimization in elderly and athletic populations. In this context, the integration of Fiber Bragg Grating (FBG) sensors into wearable systems has gained notable attention due to their lightweight, electromagnetic immunity, and high strain sensitivity [25]. Several recent studies have explored the use of FBGs for motion monitoring, including systems embedded into insoles, socks, or braces to capture localized mechanical strain related to gait dynamics [17; 18]. In a previous study, a gait monitoring system based on optical fiber, complemented by a motion capture system, was proposed. However, this system differs from the solution presented in this paper in several aspects, including the use of a plastic optical fiber (POF), a required calibration procedure, and measurements based on the transmitted optical power when the POF is bent [26]. In contrast, the system presented here uses a silica-based Fiber Bragg Grating, which offers improved spectral resolution and stability for dynamic sensing applications. Additionally, the integration of photonic and inertial modalities allows for cross-validation and redundancy, enhancing system robustness. Inertial sensors, such as accelerometers and gyroscopes, have been extensively used to monitor human kinematics during the gait process by measuring the acceleration and orientation of specific segments of the arms and legs [27; 28; 29]. However, their accuracy can be affected by environmental noise, magnetic field disturbances, or improper sensor placement. To overcome these limitations, hybrid systems combining inertial and photonic sensing have been explored, leveraging the strengths of both modalities. For example, [PERSON] et al. (2020) demonstrated a wearable system for knee monitoring based on FBGs, highlighting improved angular detection when combined with IMUs [25]. [PERSON] et al. (2022) further emphasized the utility of such hybrid systems in the early detection of neurodegenerative gait anomalies, illustrating their potential for both clinical diagnosis and sports performance tracking [30].