OAFPM: optimized ANFIS using frequent pattern mining for activity recognition

Fall causes serious perils for the elder people when they are living alone. A mathematical model, optimized ANFIS using frequent pattern mining (OAFPM) for activity recognition, is proposed in this paper, which uses fuzzy inference system, adaptive neural network and frequent pattern mining (FPM) to identify the activity of a person accurately. Accelerometer values are given as input to the proposed model in real time which forms the premise part of the model, whereas the consequence is defined by the rules generated out of input and output linear relation. Initial rule identification is done through membership functions of each activity, and the number of rules is reduced using FPM approach. During the learning phase, the optimal premise parameters are selected using gradient descent method and the choice of consequent parameters is based on the least-square estimation method. The optimal values of premise and consequent parameters along with the reduced rule matrix made the OAFPM model to achieve an accuracy rate of 95.8 % and also reduce the computational complexity by triggering less number of nodes for each activity.