Worldwide the ageing population is increasing, and there are new requirements from governments to keep people at home longer. As a consequence assisted living has been an active area of research, and radar has been identified as an emerging technology of choice for indoor activity monitoring. Activity classification has been investigated, but is often limited by the classification accuracy in the most challenging yet realistic cases. This paper aims to evaluate and improve the accuracy in classifying six commonly performed indoor activities from the University of Glasgow open dataset. For activity classification, the selection of features to discriminate between activities is paramount. Activity classification is usually done as one vs all strategy with one classifier and a set of features to distinguish between all the activities. In this paper, we propose to optimise the feature selection and classifier choice per activity using a hierarchical classification structure. This strategy reached 95.4% accuracy for all activities and about 100% for walking, opening the field for personnel recognition.