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.

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Algorithms based on the ground reflex pressure (GRF) signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA) and kernel principal component analysis (KPCA) for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM), SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

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