Measuring Functional Independence of an Aged Person with a Combination of Machine Learning and Logical Reasoning

Various approaches to human activity recognition have been proposed to achieve better management of human health and wellness. However, there are few approaches that measure the levels of activity in an accountable way. In this paper, we propose a novel approach to measure the functional independence of an aged person with a combination of machine learning and ontology-based logical reasoning. As to combining the two different approaches, we utilize semantic contexts as the interlayer and dummy contexts as a way of handling the difficulty in reasoning with incomplete data. The Functional Independence Measure (FIM) is used to build an ontology to evaluate an aged person’s functional independence. Evaluation experiments using data collected in the laboratory environment of the authors’ are conducted, and the results of which show the effectiveness of the proposed approach.