Prediction of human activities based on a new structure of skeleton features and deep learning model

Jaouedi, Neziha and Perales, Francisco J. and Buades, José Maria and Boujnah, Noureddine and Bouhlel, Med Salim (2020) Prediction of human activities based on a new structure of skeleton features and deep learning model. Sensors (Switzerland), 20 (17). pp. 1-15. ISSN 1424-8220

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Abstract

The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of several researchers. Human activities are an important challenge in various fields. There are many great applications in this area, including smart homes, assistive robotics, human–computer interactions, and improvements in protection in several areas such as security, transport, education, and medicine through the control of falling or aiding in medication consumption for elderly people. The advanced enhancement and success of deep learning techniques in various computer vision applications encourage the use of these methods in video processing. The human presentation is an important challenge in the analysis of human behavior through activity. A person in a video sequence can be described by their motion, skeleton, and/or spatial characteristics. In this paper, we present a novel approach to human activity recognition from videos using the Recurrent Neural Network (RNN) for activity classification and the Convolutional Neural Network (CNN) with a new structure of the human skeleton to carry out feature presentation. The aims of this work are to improve the human presentation through the collection of different features and the exploitation of the new RNN structure for activities. The performance of the proposed approach is evaluated by the RGB-D sensor dataset CAD-60. The experimental results show the performance of the proposed approach through the average error rate obtained (4.5%).

Item Type:	Article
Additional Information:	Funding Information: This work is supported by the Ministerio de Econom?a, Industria y Competitividad (MINECO), the AgenciaEstatal de Investigaci?n (AEI), and the European Regional Development Funds (ERDF, EU) under projects TIN2015-67149-C3-2-R (MINECO/AEI/ERDF, EU), PERGAMEX RTI2018-096986-B-C31 (MINECO/AEI/ERDF, EU), PID2019-104829RA-I00/AEI/10.13039/501100011033 (MICINN) and Telecommunication, Software and System Group (TSSG), Waterford Institute of Technology, Waterford, Ireland. Funding Information: Funding: This work is supported by the Ministerio de Economía, Industria y Competitividad (MINECO), the AgenciaEstatal de Investigación (AEI), and the European Regional Development Funds (ERDF, EU) under projects TIN2015-67149-C3-2-R (MINECO/AEI/ERDF, EU), PERGAMEX RTI2018-096986-B-C31 (MINECO/AEI/ERDF, EU), PID2019-104829RA-I00/AEI/10.13039/501100011033 (MICINN) and Telecommunication, Software and System Group (TSSG), Waterford Institute of Technology, Waterford, Ireland. Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1600/1602
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:10
Last Modified:	13 Aug 2023 22:50
URI:	http://repository-testing.wit.ie/id/eprint/4493

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