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Article

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model

1
Sciences and Technologies of Image and Telecommunications (SETIT) Laboratory, Sfax 3029, Tunisia
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Mathematics and Computer Science Department, Universitat de les Illes Balears (UIB), E-07122 Palma, Spain
3
Telecommunication Software and Systems Group (TSSG), Waterford Institute of Technology, X91 P20H Waterford, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(17), 4944; https://doi.org/10.3390/s20174944
Received: 29 June 2020 / Revised: 20 August 2020 / Accepted: 21 August 2020 / Published: 1 September 2020
(This article belongs to the Section Electronic Sensors)
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%). View Full-Text
Keywords: human activities; action recognition; skeleton features; motion tracking; human detection; deep learning; deep association metric human activities; action recognition; skeleton features; motion tracking; human detection; deep learning; deep association metric
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MDPI and ACS Style

Jaouedi, N.; Perales, F.J.; Buades, J.M.; Boujnah, N.; Bouhlel, M.S. Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model. Sensors 2020, 20, 4944. https://doi.org/10.3390/s20174944

AMA Style

Jaouedi N, Perales FJ, Buades JM, Boujnah N, Bouhlel MS. Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model. Sensors. 2020; 20(17):4944. https://doi.org/10.3390/s20174944

Chicago/Turabian Style

Jaouedi, Neziha, Francisco J. Perales, José M. Buades, Noureddine Boujnah, and Med S. Bouhlel. 2020. "Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model" Sensors 20, no. 17: 4944. https://doi.org/10.3390/s20174944

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