Research Article
A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors
@INPROCEEDINGS{10.4108/icst.bodynets.2011.247018, author={Mi Zhang and Alexander Sawchuk}, title={A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors}, proceedings={6th International ICST Conference on Body Area Networks}, publisher={ICST}, proceedings_a={BODYNETS}, year={2012}, month={6}, keywords={feature design feature selection human activity recognition wearable multimodal sensors pattern recognition}, doi={10.4108/icst.bodynets.2011.247018} }- Mi Zhang
Alexander Sawchuk
Year: 2012
A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors
BODYNETS
ICST
DOI: 10.4108/icst.bodynets.2011.247018
Abstract
Human activity recognition is important for many applications. This paper describes a human activity recognition framework based on feature selection techniques. The objective is to identify the most important features to recognize human activities. We first design a set of new features (called physical features) based on the physical parameters of human motion to augment the commonly used statistical features. To systematically analyze the impact of the physical features on the performance of the recognition system, a single-layer feature selection framework is developed. Experimental results indicate that physical features are always among the top features selected by different feature selection methods and the recognition accuracy is generally improved to 90%, or 8% better than when only statistical features are used. Moreover, we show that the performance is further improved by 3.8% by extending the single-layer framework to a multi-layer framework which takes advantage of the inherent structure of human activities and performs feature selection and classification in a hierarchical manner.