6th International ICST Conference on Body Area Networks

Research Article

A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors

Download356 downloads
  • @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
Mi Zhang1,*, Alexander Sawchuk1
  • 1: University of Southern California
*Contact email: mizhang@usc.edu

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.