An integrated pattern recognition system for knee flexion analysis

Abstract

The purpose of this study is to propose an integrated knee-flexion analysis system (IKAS) as a novel tool for recognition pattern of knee muscle for athletes and soldiers based on neuromuscular signals and soft tissue deformation parameter. Different types of parameters from multi-sensors integration are combined to analyze the knee motion. Data fusion of EMG and frames of the video for each knee flexion angle acquired from synchronization of the motion capture system and video cameras interfaced with wireless EMGsensors. Systems are pre-processed in order to prepare the pattern set for a custom-developed artificial neural network and mesh generation technique based intelligent system for classifying the patterns of knee muscle of subjects during walking and squatting activity. Multilayer feed-forward backpropagation networks (FFBPNNs) with different network training algorithm were designed and coefficient correlation (CC) was uses and their classification results were compared. The newly introduced IKAS approach will provides assistance in making an objective and knowledgeable decisions about recognition of patterns from knee muscles.