MIMU-Wear: Ontology-based sensor selection for real-world wearable activity recognition

An enormous effort has been made during the recent years towards the recognition of human activity based on wearable sensors. Despite the wide variety of proposed systems, most existing solutions have in common to solely operate on predefined settings and constrained sensor setups. Real-world activity recognition applications and users rather demand more flexible sensor configurations dealing with potential adverse situations such as defective or missing sensors. In order to provide interoperability and reconfigurability, heterogeneous sensors used in wearable activity recognition systems must be fairly abstracted from the actual underlying network infrastructure. This work presents MIMU-Wear, an extensible ontology that comprehensively describes wearable sensor platforms consisting of mainstream magnetic and inertial measurement units (MIMUs). MIMU-Wear describes the capabilities of MIMUs such as their measurement properties and the characteristics of wearable sensor platforms including their on-body location. A novel method to select an adequate replacement for a given anomalous or nonrecoverable sensor is also presented in this work. The proposed sensor selection method is based on the MIMU-Wear Ontology and builds on a set of heuristic rules to infer the candidate replacement sensors under different conditions. Then, queries are iteratively posed to select the most appropriate MIMU sensor for the replacement of the defective one. An exemplary application scenario is used to illustrate some of the potential of MIMU-Wear for supporting seamless operation of wearable activity recognition systems. (C) 2017 Elsevier B.V. All rights reserved.