Human Moving Pattern Recognition toward Channel Number Reduction Based on Multipressure Sensor Network

Abstract

A pair of sensing shoes for measuring foot pressure was developed. This system aims at recognizing human movement in unlimited environments. The multipressure sensor network of seven sensors on one insole was set up. Analysis for discriminating the user's movements from foot pressure distribution was carried out, considering the movements of standing, walking, going upstairs, and going downstairs. These actions were discriminated using characteristics extracted from the data of sensors. The classifier based on SVM showed highly accurate movement recognition. Specifically, to improve the classification performance, PCA based dimensionality reduction and channel reduction based data fusion were introduced. Experimental outcomes verified the testing speed of the classification function which was improved without affecting the accuracy rate. The results confirmed that this discriminant analysis can be employed for automatically recognizing human moving pattern based on foot pressure signal.