Fiber Bragg Grating Accelerometer-Based Feature Extraction for Gait Analysis

Understanding human movement patterns and evaluating a range of medical disorders depend heavily on the analysis of gait. In this study, we propose a new method for gait analysis, based on multiplexed fiber Bragg grating (FBG) accelerometers. Our work expands the capabilities of FBG-based accelerometers by extracting gait features through the analysis of output signals. In contrast to traditional wearable sensors, our solution offers scalability and discreet monitoring while integrating smoothly into the current infrastructure. Step duration, cadence, peak acceleration, and gait symmetry are among the critical gait metrics that we calculate using MATLAB-based methods to preprocess the accelerometer data. Experiments show that our method is a good fit for precisely capturing gait dynamics. The study revealed significant differences among individuals (p<0.05) in gait parameters based on height and age groups, indicating variations in step time, and normalized cadence. Our findings have important ramifications for biometric identification, rehabilitation, and healthcare applications.