Abstract
Smartphone sensor-based human activity recognition (SSHAR) generally deals with three main steps: 1) raw signal collection; 2) feature extraction; and 3) human activity classification. This study focuses on an interpretable human activity classification method to enhance SSHAR's very applicability for the application scenarios like healthcare services and personal biometric signature. To this end, by taking Takagi-Sugeno-Kang fuzzy classifiers as the subclassifiers, a novel interpretable ensemble fuzzy classifier FINE is proposed to provide linguistically interpretable fuzzy rules for classification, strong generalization and scalability for SSHAR. Since each subclassifier of FINE works on its bootstrapping subspace of original features and then is combined without an explicit aggregation, FINE has the following characteristics: 1) the diversities among all the subclassifiers are assured; 2) more generalization capabilities than the corresponding structure of each subclassifier on all the input features is justified; 3) its incremental learning can be implemented through only training an incremental subclassifier or training FINE only on incremental data. The experimental results demonstrate that FINE not only keeps at least comparable to and even better than most of the comparative methods in terms of testing performance and training time but also has both linguistically interpretable fuzzy rules and fast incremental learning capability.
