Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition
Journal
Lecture Notes in Computer Science
Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information
ISSN
0302-9743
1611-3349
Date Issued
2015
Author(s)
Miralles-Pechuán, Luis
Type
Resource Types::text::book::book part
Abstract
In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.