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

Using Dempster-Shafer Theory for RSS-based Indoor Localization

International audienceWith the proliferation of the Internet of Things (IoT), employing Received Signal Strength (RSS) as a metric to determine the location of a target (e.g., person or mobile device) is of great interest in terms of cost and ease of implementation. Indeed, RSS measurements can be easily obtained for most off-the-shelf devices, such as WiFi- or ZigBee compatible devices or sensors. This paper deals with the indoor localization problem in wireless sensor networks (WSNs) and proposes a new approach for radio signal propagation modelling and localization estimation, that accounts for the imperfection of RSS measurements and the reliability of RSS sources by using the Dempster-Shafer Theory (DST). In the signal propagation modelling, key information regarding the geometry of indoor environment that is divided into zones separated by walls (zoning), are considered. Based on the number of walls, the RSS irregularities are estimated using different distance intervals, which are weighted by a probability density determined experimentally. To estimate the location of a target node, the PCR6 rule is used to combine the belief masses of the positions obtained from the probability density. In order to evaluate the performance of the proposed approach, an experimental WSN has been deployed in a living apartment. The obtained results demonstrate that the proposed approach improves the localization accuracy compared to the case without zoning. Moreover, the obtained localization mean error proves the feasibility of a precise localization of humans in indoor environments in the case of Ambient Assisted Living and Social Robotics applications

Ontology-based hybrid commonsense reasoning framework for handling context abnormalities in uncertain and partially observable environments

International audienceAmbient intelligence (AmI) systems aim to provide users with context-aware assistance services intended to improve the quality of their lives in terms of autonomy, safety, and well-being. Taking the uncertainty and partial observability of these environments into account is of major importance for context recognition and, more specifically, to detect and solve context abnormalities such as those related to the user's behavior or those related to context attribute prediction. In this paper, an ontology-based framework integrating machine learning and probabilistic planning within commonsense reasoning is proposed to recognize the user's context and abnormalities associated with it. The reasoning is performed using event calculus in answer set programming (ECASP); ECASP allows for abductive and temporal reasoning, which results in an eXplainable AI (XAI) approach. A context ontology is proposed to axiomatize the reasoning and introduce the notion of probabilistic fluents into the EC formalism in order to perform probabilistic reasoning. The reasoning incorporates probabilistic planning based on a partially observable Markov decision process (POMDP) to solve knowledge incompleteness. To evaluate the proposed framework, real-life scenarios, based on the Orange4Home and SIMADL public datasets are implemented and discussed

Context-aware Adaptive Recommendation System for Personal Well-being Services

International audienceNowadays, a healthy lifestyle is an essential requirement in people's daily life. Although well-being recommendation systems have been extensively explored in different domains, there are still some challenges for developing efficient recommendation systems dealing with the limitations of content-based recommendation approaches. In this paper, a context-aware adaptive recommendation system is proposed to provide personal wellbeing services intended to help people to have a healthy lifestyle in Ambient Assisted Living (AAL) systems. The recommendations are based on people's behaviors. Machine-learning models are firstly used to recognize human activities, locations, and objects. The different contexts of human behaviors, including location, object, frequency, duration, and sequences of frequent activities, are then extracted. An ontology, called Human ActiVity ONtology (HAVON) ontology, is used to conceptualize human activities and their contexts. Finally, a probabilistic version of Answer set Programming (ASP), a high-level expressive logic-based formalism, is proposed to provide adaptive recommendations through a set of probabilistic rules based on human behaviors. A companion robot, called Pepper, is used for the evaluation of the proposed recommendation system. The evaluation results demonstrate the ability of the proposed system to help people to have a healthy lifestyle

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)

Hybrid approach for anticipating human activities in Ambient Intelligence environments

International audienceRecognising the human context in terms of ongoing human activities is of major importance to ensure an efficient context-aware assistance. In this paper, a hybrid approach combining deep learning and probabilistic commonsense reasoning is proposed for anticipating human activities in AmI environments. Deep learning models are exploited for recognising environment objects, human hands and user’s indoor locations. To implement probabilistic commonsense reasoning, probabilistic fluents are introduced in the formalism of event calculus formulated in answer set programming (ECASP). The reasoning axiomatization is based on an ontology describing the user’s context when performing an activity. Using reasoning based on temporal projection and abduction enables an eXplainable AI (XAI) approach for activity anticipation. Experimental results show the high accuracy of inferences in terms of activity anticipation and a very low computation time in knowledge-intensive scenarios, rendering the system compatible with real-time applications

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

A Context-aware Hybrid Framework for Human Behavior Analysis

International audienceIn Ambient Assisted Living (AAL) systems and personal assistive robots, human behavior analysis is essential to provide intelligent services intended to improve people's quality of lives in terms of autonomy, well-being, and safety. Human behavior analysis allows discovering people's preferences, activities, and habits. While human behavior analysis has been explored in several domains, there are still some challenges for developing efficient approaches dealing with the limitations of data-driven approach to analyze human behaviors. In this paper, a framework is proposed to better characterize the human context by inferring new knowledge about his/her behaviors using commonsense reasoning and exploiting contextual information. Human activities are firstly recognized using a CNN-LSTM model. Different contexts of human activities are then extracted to analyze human behaviors. The obtained activity contexts are mapped to an ontology, called Human AcTivity (HAT) ontology, conceptualizing the human activities and their contexts. Answer Set Programming (ASP), a high-level expressive logic-based formalism, is then used to represent human behaviors and carry out commonsense reasoning to infer new knowledge about these behaviors. The proposed framework is evaluated using the Orange4Home dataset. Moreover, two quantitative experiments are carried out to demonstrate the ability of the proposed framework to better characterize human behaviors