Neila Mezghani

M. Abid, A. Khabou, Y. Ouakrim et al.

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification, to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yields an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down.

Mohamed El Amine Elforaici, Ismail Chaaraoui, Wassim Bouachir et al.

The emergence of RGB-D sensors offered new possibilities for addressing complex artificial vision problems efficiently. Human posture recognition is among these computer vision problems, with a wide range of applications such as ambient assisted living and intelligent health care systems. In this context, our paper presents novel methods and ideas to design automatic posture recognition systems using an RGB-D camera. More specifically, we introduce two supervised methods to learn and recognize human postures using the main types of visual data provided by an RGB-D camera. The first method is based on convolutional features extracted from 2D images. Convolutional Neural Networks (CNNs) are trained to recognize human postures using transfer learning on RGB and depth images. Secondly, we propose to model the posture using the body joint configuration in the 3D space. Posture recognition is then performed through SVM classification of 3D skeleton-based features. To evaluate the proposed methods, we created a challenging posture recognition dataset with a considerable variability regarding the acquisition conditions. The experimental results demonstrated comparable performances and high precision for both methods in recognizing human postures, with a slight superiority for the CNN-based method when applied on depth images. Moreover, the two approaches demonstrated a high robustness to several perturbation factors, such as scale and orientation change.