Deriving Effective Human Activity Recognition Systems through Objective Task Complexity Assessment

Research in sensor based human activity recognition (HAR) has been a core concern of the mobile and ubiquitous computing community. Sophisticated systems have been developed with the main view on applications of HAR methods in research settings. This work addresses a related yet practically different problem that mainly focuses on users of HAR technology. We acknowledge that practitioners from outside the core HAR research community are motivated to employ HAR methods for practical deployments. Even though standard processing approaches exist, arguably, often times substantial modifications are necessary to derive effective analysis systems. It is not always clear a-priori how challenging a HAR task actually is and what dimensions of an analysis pipeline are crucial for successful automated assessments. In practice this can lead to disappointing results or disproportionate efforts that have to be invested into the optimization of data analysis pipelines, that were supposed to work "out of the box". We present a framework for the objective complexity assessment of HAR tasks that directly supports practitioners' decision making of whether and how to employ HAR for their deployments. We map a HAR task onto a vectorial representation that allows us to analyse the inherent challenges of the task and to draw conclusions through similarity analysis with regards to existing tasks. We validate our complexity assessment framework on 23 HAR datasets and derive a data-driven categorization of human activity recognition. We demonstrate how our objective analysis can be used to inform the deployment of HAR systems in practical scenarios.