Deep HMResNet Model for Human Activity-Aware Robotic Systems

Endowing the robotic systems with cognitive capabilities for recognizing daily activities of humans is an important challenge, which requires sophisticated and novel approaches. Most of the proposed approaches explore pattern recognition techniques which are generally based on hand-crafted features or learned features. In this paper, a novel Hierarchal Multichannel Deep Residual Network (HMResNet) model is proposed for robotic systems to recognize daily human activities in the ambient environments. The introduced model is comprised of multilevel fusion layers. The proposed Multichannel 1D Deep Residual Network model is, at the features level, combined with a Bottleneck MLP neural network to automatically extract robust features regardless of the hardware configuration and, at the decision level, is fully connected with an MLP neural network to recognize daily human activities. Empirical experiments on real-world datasets and an online demonstration are used for validating the proposed model. Results demonstrated that the proposed model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606

Intergiciel multi agents orienté web sémantique pour le développement d applications ubiquitaires sensibles au contexte

PARIS12-Bib. électronique (940280011) / SudocSudocFranceF

Automatic Recognition of Gait phases Using a Multiple Regression Hidden Markov Model

International audienceThis paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a Multiple Regression Hidden Markov Model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem in which each phase, called a segment, is modelled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labelling, which is a laborious, time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall and precision. Experiments conducted with 5 subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (GMM, k-Means and HMM) while remaining competitive with respect to standard supervised classification methods (SVM, RF and k-NN)

Towards Semantic Multimodal Emotion Recognition for Enhancing Assistive Services in Ubiquitous Robotics

International audienceIn this paper, the problem of endowing ubiquitous robots withcognitive capabilities for recognizing emotions, sentiments,affects and moods of humans, in their context, is studied. Ahybrid approach based on multilayer perceptron (MLP) neural network and n-ary ontologies for emotion-aware roboticsystems is proposed. In particular, an algorithm based on thehybrid-level fusion, an expressive emotional knowledge representation and reasoning model are introduced to recognizecomplex and non-observable emotional context of the user.Empirical experiments on real-world dataset corroborate itseffectiveness

IoRT cloud survivability framework for robotic AALs using HARMS

International audienceThe Internet of Robotic Things, which includes ambient assisted living systems has been pushed to be developed by the research community for reasons such as the population gap between elderly people and their caregivers. Due to the critical mission that is assigned to those systems; interruptions, failures, worse still, full malfunction should not be allowed to materialize. Such systems ought to keep running in a proper way notwithstanding problems caused either by internal and external system collapses or bad intentioned actions in their surroundings. Therefore, including survivability features must be insured to Ambient Assisted Living systems (AALs) using Humans, software Agents, Robots, Machines, and Sensors (HARMS). HARMS stands for the model that allows through the indistinguishability feature to any type of actor to communicate and interact. This work proposes a framework which takes advantage of the Cloud to overcome the state explosion problem encountered when using model checking. Model checking techniques are used to find a possible solution when a problem is already faced by the system — instead of its original purpose to detect errors on the systems during the design stage. This paper presents the implementation of the proposed framework and validates the functionality with experiments. The conducted experiments evaluate the advantages of using cloud tools to offload the model checking capability for applications such as multi-agent systems

Reinforcement Learning for Interactive QoS-Aware Services Composition

International audienceAn important and challenging research problem in web of things is how to select an appropriate composition of concrete services in a dynamic and unpredictable environment. The main goal of this article is to select from all possible compositions the optimal one without knowing a priori the users' quality of service (QoS) preferences. From a theoretical point of view, we give bounds on the problem search space. As the QoS user's preferences are unknown, we propose a vector-valued MDP approach for finding the optimal QoS-aware services composition. The algorithm alternatively solves MDP with dynamic programming and learns the preferences via direct queries to the user. An important feature of the proposed algorithm is that it is able to get the optimal composition and, at the same time, limits the number of interactions with the user. Experiments on a real-world large size dataset with more than 3500 web services show that our algorithm finds the optimal composite services with around 50 interactions with the user