Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted
increasing attention since the first work reported in 2010. Over this period, many benchmark …

In this paper, we present a method (Action-Fusion) for human action recognition from depth
maps and posture data using convolutional neural networks (CNNs). Two input descriptors …

Computational representation of everyday emotional states is a challenging task and,
arguably, one of the most fundamental for affective computing. Standard practice in emotion …

Activity prediction is an essential task in practical human-centered robotics applications,
such as security, assisted living, etc., which is targeted at inferring ongoing human activities …

In a natural and accessible Human Robot Interaction (HRI), it is required to understand
human activities instantly. In this paper, we present a novel approach for early recognition of …

The goal of this work is to forecast human activities that may require robot assistance. Each
activity consists of consecutive actions. Each action is bounded by initial and final state and …

We propose a novel approach toward event detection in real-world continuous video
sequences. The method: 1) is able to model arbitrary-order non-Markovian dependences in …

My PhD work aims at developing computational methodologies for automatic emotion
recognition from audiovisual behavioral data. A main challenge in automatic emotion …

In human interaction, understanding human behaviors is a challenging problem in todays
world. Action recognition has become a very important topic in detecting the emotional …

Human Activity Recognition (HAR) based on accelerometer has become an important
mobile application. Activity recognition however depends on X, Y, Z the Cartesian …