A first step towards explained activity recognition with computational abstract argumentation

Xiuyi Fan, Huiguo Zhang, Cyril Leung, Chunyan Miao

Abstract: Activity recognition is a key problem in multisensor systems. In a home-like environment, from several sensors of different types, the multi-sensor system identifies activities performed by the inhabitants. Many supervised learning techniques exist for solving this problem. In this paper, we present a novel argumentation based approach that seamlessly combines low level sensor data processing, realized with Neural Network classifiers with high level activity recognition, represented by argumentation computation. The proposed framework gives classification results comparable to pure learning based approaches with significantly reduced training time while giving argumentative explanations.