Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

In the present scenario like COVID-19 pandemic, to maintain physical distance, gait-based biometric is a must. Human gait identification very difficult process, but it suitable distance that also gives good results at low resolution conditions even with face features are not clear. This study describes construction of smart carpet measures ground response force (GRF) and spatio-temporal parameters (STGP) using polymer optical fiber sensor (POFS). The suggested contains two light detection units for acquiring signals. Each unit obtains from 10 nearby sensors. There 20 intensity deviation sensors on fiber. Light-emitting diodes (LED) triggered successively, multiplexing approach being employed. Multiplexing dependent coupling among LED POFS sections. Results walking experiments performed certain parameters, including step length, stride cadence, stance time, might be used estimate GRF STGP. enable gait, swing phase, stance, double supporting periods. dependable, reasonably priced equipment acquisition in variety applications. Using data, recognition genetic algorithm (GA) particle swarm optimization (PSO) technique. GA- PSO-based template analyses extract respect signals obtained (POGS). techniques classification random forest (RF) support vector machine (SVM). accuracy, sensitivity, specificity SVM classifier RF classifier. both classifiers compared.