Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rech...

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Bibliographic Details
Published in:Expert systems with applications 2014-10, Vol.41 (14), p.6138-6146
Main Authors: Álvarez de la Concepción, M.A., Soria Morillo, L.M., Gonzalez-Abril, L., Ortega Ramírez, J.A.
Format: Article
Language:English
Subjects:
Accelerometers
Applied sciences
Battery
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Data processing. List processing. Character string processing
Discretization method
Energy saving
Exact sciences and technology
Expert systems
Human
Human motion
Memory organisation. Data processing
Mobile communication systems
Neural networks
Pattern recognition
Qualitative systems
Rechargeable batteries
Recognition
Smart-energy computing
Software
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Summary:Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2014.04.018