Title: LAHAR-CNN: human activity recognition from one image using convolutional neural network learning approach

Authors: Hend Basly; Wael Ouarda; Fatma Ezahra Sayadi; Bouraoui Ouni; Adel M. Alimi

Addresses: Networked Objects Control and Communication Systems Laboratory (NOCCS-Lab), National Engineering School of Sousse (ENISO), University of Sousse, BP 264 Erriadh, Sousse, 4023, Tunisia ' Research Groups in Intelligent Machines (REGIM-Lab), National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038, Tunisia ' Electronics and Microelectronics Laboratory (EμE-Lab), Faculty of Sciences of Monastir (FSM), University of Monastir, Environment Avenue, Monastir, 5019, Tunisia ' Networked Objects Control and Communication Systems Laboratory (NOCCS-Lab), National Engineering School of Sousse (ENISO), University of Sousse, BP 264 Erriadh, Sousse, 4023, Tunisia ' Research Groups in Intelligent Machines (REGIM-Lab), National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038, Tunisia

Abstract: The problem of human action recognition has attracted the interest of several researchers due to its significant use in many applications. With the great success of deep learning methods in most areas, researchers decided to switch from traditional methods-based hand-crafted feature extractors to recent deep learning-based techniques to recognise the action. In the present research work, we propose a learning approach for human activity recognition in the elderly based on convolutional neural network (LAHAR-CNN). The CNN model is used to extract features from the dataset, then, a multilayer perceptron (MLP) classifier is used for action classification. It has been widely admitted that features learned using a CNN model on a large dataset can be successfully transferred to an action recognition task with a small training dataset. The proposed method is evaluated on the well-known MSRDailyActivity 3D dataset. It has shown impressive results that exceed the performances obtained in the state of the art using the same dataset, thus reaching 99.4%. Furthermore, our proposed approach predicts human activity (HA) from one single frame sample which justifies its robustness. Hence, the proposed model is ranked at the top of the list of space-time techniques.

Keywords: human activity recognition; convolutional neural network; CNN; deep learning; daily living activity.

DOI: 10.1504/IJBM.2021.117855

International Journal of Biometrics, 2021 Vol.13 No.4, pp.385 - 408

Received: 13 Dec 2019
Accepted: 26 Jun 2020
Published online: 04 Oct 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article