Hierarchical evolutionary classification framework for human action recognition using sparse dictionary optimization

Human action recognition using wearable sensors play a remarkable role in the province of Human-centric Computing. This paper accords a novel sparse representation based hierarchical evolutionary framework for classifying human activities. The main objective of this research is to propose a model for human action recognition that produces superior recognition results. This framework employs data from two inertial sensors namely an accelerometer and a gyroscope. Features like time and frequency-domain features were utilized in this work. This framework operates at multiple levels in the hierarchy, wherein, the output of one level is given as input to the next level in the hierarchy. A novel algorithm for deducing the hierarchical structure based on the input data called Hierarchical Architecture Design (HAD) algorithm is presented. We have also presented a novel Sparse Dictionary Optimization (SDO) algorithm for generating dictionaries that aid in the efficacious sparse representation-based classification. Finally, action recognition is done using the proposed Sparse Representation based Hierarchical (SRH) classifier. The performance analysis of the proposed system was conducted using University of Southern California Human Activity Dataset (USC -HAD) and Human Activities and Postural Transitions (HAPT) Dataset. The proposed classification framework attained a very high F-score value of 98.01% and 93.51% for the USC -HAD and HAPT datasets respectively.

Automated summary

This paper proposes a novel sparse representation based hierarchical evolutionary framework for classifying human activities, using data from sensors such as accelerometers and gyroscopes. The framework operates at multiple levels in the hierarchy and achieves high recognition results through novel algorithms and optimization techniques.