Representação simbólica de séries temporais para reconhecimento de atividades humanas no smartphone

Abstract

Human activity recognition (RAH) through sensors embedded in wearable devices such as smartphones has allowed the development of solutions capable of monitoring human behavior. However, such solutions have presented limitations in terms of efficiency in the consumption of computational resources and generalization for different application or data domain configurations. These limitations are explored in this work in the feature extraction process, in which existing solutions use a manual approach to extract the characteristics of the sensor data. To overcome the problem, this work presents an automatic approach to feature extraction based on the symbolic representation of time series --- representation defined by sets of discrete symbols (words). In this context, this work presents an extension of the symbolic representation of the Bag-Of-SFA-Symbols (BOSS) method to handle the processing of multiple time series, reduce data dimensionality and generate compact and efficient classification models. The proposed method, called Multivariate Bag-Of-SFA-Symbols (MBOSS), is evaluated for the classification of physical activities from data of inertial sensors. Experiments are conducted in three public databases and for different experimental configurations. In addition, the efficiency of the method is evaluated in aspects such as computing time and data space. The results, in general, show an efficiency of classification equivalent to the solutions based on the traditional approach of manual extraction, highlighting the results obtained in the database with nine classes of activities (UniMib SHAR), where MBOSS obtained an accuracy of 99% and 87% for the custom and generalized template, respectively. The efficiency results of MBOSS demonstrate the low computational cost of the solution and show the feasibility of application in smartphones.