Khan, Aftab and Mellor, Sebastian and Berlin, Eugen and Thompson, Robin and McNaney, Roisin and Olivier, Patrick and Plötz, Thomas (2015) Beyond activity recognition:skill assessment from accelerometer data. In: UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, JPN, pp. 1155-1166. ISBN 9781450335744
Full text not available from this repository.Abstract
The next generation of human activity recognition applications in ubiquitous computing scenarios focuses on assessing the quality of activities, which goes beyond mere identification of activities of interest. Objective quality assessments are often difficult to achieve, hard to quantify, and typically require domain specific background information that bias the overall judgement and limit generalisation. In this paper we propose a framework for skill assessment in activity recognition that enables automatic quality analysis of human activities. Our approach is based on a hierarchical rule induction technique that effectively abstracts from noise-prone activity data and assesses activity data at different temporal contexts. Our approach requires minimal domain specific knowledge about the activities of interest, which makes it largely generalisable. By means of an extensive case study we demonstrate the effectiveness of the proposed framework in the context of dexterity training of 15 medical students engaging in 50 attempts of surgical activities.