A state classification method based on space-time signal processing using SVM for wireless monitoring systems

Abstract

In this paper we focus on improving state classification methods that can be implemented in elderly care monitoring systems. The authors group has previously proposed an indoor monitoring and security system (array sensor) that uses only one array antenna as the receiver. The clear advantages over conventional systems are improvement of privacy concern from the usage of closed-circuit television (CCTV) cameras, and elimination of installation difficulties. Our approach is different from the previous detection method which uses an array of sensors and a threshold that can classify only two states: nothing and something happening. In this paper, we present a state classification method that uses only one feature obtained from the radio wave propagation, and assisted by multiclass support vector machines (SVM) to classify the occurring states. The feature is the first eigenvector that spans the signal subspace of interest. The proposed method can be applied to not only indoor environments but also outdoor environments such as vehicle monitoring system. We performed experiments to classify seven states in an indoor setting: “No event,” “Walking,” “Entering into a bathtub,” “Standing while showering,” “Sitting while showering,” “Falling down,” and “Passing out;” and two states in an outdoor setting: “Normal state” and “Abnormal state.” The experimental results show that we can achieve 96.5 % and 100 % classification accuracy for indoor and outdoor settings, respectively.