A benchmark study on feature selection for human activity recognition
Katerina Karagiannaki, Αθανασία Πανουσοπούλου, Panagiotis Tsakalides · 2016
Human Activity Recognition (HAR) currently confronts the challenge of interpreting massive data streams to a significantly smaller number of activities. Thus, feature selection should be treated as an inseparable aspect of the HAR chain. In this work we perform an integrated study on feature selection, considering: (a) the generation of an expanded HAR dataset; (b) the development of a software tool that covers the entire feature-level fusion chain; (c) the calculation of performance metrics that go beyond machine learning terms. The results yield guidelines on the preferable feature selection technique that should be considered for adoption in the HAR domain.