Quantitative method for gait pattern detection based on fiber Bragg grating sensors

This paper presents a method that uses fiber Bragg grating (FBG) sensors to distinguish the temporal gait patterns in gait cycles. Unlike most conventional methods that focus on electronic sensors to collect those physical quantities (i.e., strains, forces, pressure, displacements, velocity, and accelerations), the proposed method utilizes the backreflected peak wavelength from FBG sensors to describe the motion characteristics in human walking. Specifically, the FBG sensors are sensitive to external strain with the result that their backreflected peak wavelength will be shifted according to the extent of the influence of external strain. Therefore, when subjects walk in different gait patterns, the strains on FBG sensors will be different such that the magnitude of the backreflected peak wavelength varies. To test the reliability of the FBG sensor platform for gait pattern detection, the gold standard method using force-sensitive resistors (FSRs) for defining gait patterns is introduced as a reference platform. The reliability of the FBG sensor platform is determined by comparing the detection results between the FBG sensors and FSRs platforms. The experimental results show that the FBG sensor platform is reliable in gait pattern detection and gains high reliability when compared with the reference platform.