LAHAR-CNN: human activity recognition from one image using convolutional neural network learning approach
by Hend Basly; Wael Ouarda; Fatma Ezahra Sayadi; Bouraoui Ouni; Adel M. Alimi
International Journal of Biometrics (IJBM), Vol. 13, No. 4, 2021

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.

Online publication date: Mon, 04-Oct-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email