The focus of this work is to investigate how children's perception of the robot changes with age and gender, and to enable the robot to adapt to these differences for improving human-robot interaction (HRI). We propose a neural network-based learning architecture to estimate children's age and gender based on the body motion performing a set of actions. To evaluate our system, we collected a fully annotated depth dataset of 28 children (aged between 7 and 16 years old) and applied it to a learning-based method for age and gender estimation by modeling children's 3D skeleton motion data. We discuss our results that show an average accuracy of 95.2% and 90.3% for age and gender respectively in the context of a real-world scenario.

This paper presents a new system which provides services to elderly and persons suffering from motor or cognitive impair-ments in a smart home (SH). SH are alternative solutions in order to keep elderly and impaired persons as long as possible at their homes to allow them to live with more comfort. SH are dynamically evolving environments, thus the provided services by this system are context aware and customizable for every user. These services can be accessed by users through an application installed on a mobile device. The sys-tem uses a multi agent system (MAS) to have a dynamic and adaptive response to environmental change. Experiments are carried out in order to validate the chosen solutions.

Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms. This paper shows that deep activity recognition models (a) provide better recognition accuracy of human activities, (b) avoid the expensive design of handcrafted features in existing systems, and (c) utilize the massive unlabeled acceleration samples for unsupervised feature extraction. We show substantial recognition improvement on real world datasets over state-of-the-art methods of human activity recognition using triaxial accelerometers.

This paper is part of the effort to develop assistive smart homes able to monitor the daily life activity of a resident and provide punctual assistance when necessary. One of the limitations of assistive smart homes is the fact that it cannot assist the resident when he is going out. Because of this, many researchers are working on wearable sensors to keep track of the activities outside the home. Our lab proposes to instead focus on smart phones which are a cheap alternative that many persons already carry in their daily life. While most algorithms used in the smart home can be exploited, smart phones generate spatial information from the GPS that do not scale very well. The goal of this paper is to initiate a discussion on spatial features and their exploitation for data mining of smart phones datasets.

We advocate a case-supported principle-based behavior paradigm coupled with the Fractal robot architecture as a means to control an eldercare robot. The most ethically preferable action at any given moment is determined using a principle, abstracted from cases where a consensus of ethicists exists.

Online social communities are becoming increasingly popular platforms for people to share information, seek emotional support, and maintain accountability for losing weight. Studying the discourse in these communities can offer insights on how users benefit from using these applications. This paper presents an analysis of language and discourse patterns in forum posts by users who lose weight and keep it off versus users with fluctuating weight dynamics. In contrast to prior studies, we have access to the weekly self-reported check-in weights of users along with their forum posts. This paper also presents a study on how goal-oriented forums are different from general online forums in terms of language markers. Our results reveal dierences about how the types of posts made by users vary along with their weight-loss patterns. These insights are closely related to the power dynamics of social interactions and can enable better design ofweight-loss applications thereby contributing to a healthy society.

As robots evolve into an integral part of the human ecosystem, humans and robots will be involved in a multitude of collaborative tasks that require complex coordination and cooperation. Indeed there has been extensive work in the robotics, planning as well as the human-robot interaction communities to understand and facilitate such seamless teaming. However, it has been argued that their increased participation as independent autonomous agents in hitherto human-habited environments has introduced many new challenges to the view of traditional human-robot teaming. When robots are deployed with independent and often self-sufficient tasks in a shared workspace, teams are often not formed explicitly and multiple teams cohabiting an environment interact more like colleagues rather than teammates. In this paper, we formalize these differences and analyze metrics to characterize autonomous behavior in such human-robot cohabitation settings.

Funding agencies and the National Academies of Science, Engineering, and Medicine have been promoting the importance of interdisciplinary research (IDR). Supporting team-based IDR requires the ability to discover the expertise needed to solve complex problems. Many universities have adopted expertise systems, which includes the presentation of keywords or concepts to identify experts. The efforts at University of Texas at El Paso (UTEP) have focused on building “communities of practice” that support diverse faculty who have an affinity for a particular topic and facilitate the ability to identify researchers with diverse expertise, knowledge, and skills who can contribute to new initiatives on campus. Our premise is that the university can facilitate the identification of potential contributors to communities of practice by correlating their associated ontologies to the concepts associated with researchers’ publications and proposal submissions. This paper presents the results of a preliminary study to examine the feasibility of the approach.

In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an interaction. This work focuses on the problem of recognizing the strategy used by an agent within a small number of interactions. We propose using a Bayesian framework to address this problem. Bayesian policy reuse (BPR) has been empirically shown to be efficient at correctly detecting the best policy to use from a library in sequential decision tasks. In this paper we extend BPR to adversarial settings, in particular, to opponents that switch from one stationary strategy to another. Our proposed extension enables learning new models in an online fashion when the learning agent detects that the current policies are not performing optimally. Experiments presented in repeated games show that our approach is capable of efficiently detecting opponent strategies and reacting quickly to behavior switches, thereby yielding better performance than state-of-the-art approaches in terms of average rewards.

We present a system that provides an immersive experience to a remote participant collaborating with other participants using a technologically advanced "smart" meeting room. Traditional solutions for virtual collaboration, such as video conferencing or chat rooms, do not allow remote participants to access or control the technological capabilities of such rooms. In this work, we demonstrate a working system for immersive virtual telepresence in a smart conference room that does allow such control.