Adaptive fuzzy C-means for human activity recognition

Fuzzy Logic is a multi-valued logic where the truth values lies between zero and one. In any system there are two phases namely the training (learning) phase and the testing phase. In the training or learning phase the data samples are given as input to the system for training the fuzzy system, to classify the inputs according to the characteristics of the problem. In the testing phase the data instances are given as input to check whether the system classifies correctly. Considering the above two phases the training phase consumes a large amount of time. In order to classify the input samples, this paper proposes the Fuzzy C-Means (FCM) for classification purpose. The FCM has number of clusters equal to the number of classes to which the input samples are to be classified, where the membership function plays a vital role in converging the number of iterations. The training time of the fuzzy system is improved through the adaptive skipping method. This paper focuses on the Human Activity Recognition (HAR) in which the human activities are to be classified.