Architecture de contrôle d'un robot de téléprésence et d'assistance aux soins à domicile

La population vieillissante provoque une croissance des coûts pour les soins hospitaliers. Pour éviter que ces coûts deviennent trop importants, des robots de téléprésence et d’assistance aux soins et aux activités quotidiennes sont envisageables afin de maintenir l’autonomie des personnes âgées à leur domicile. Cependant, les robots actuels possèdent individuellement des fonctionnalités intéressantes, mais il serait bénéfique de pouvoir réunir leurs capacités. Une telle intégration est possible par l’utilisation d’une architecture décisionnelle permettant de jumeler des capacités de navigation, de suivi de la voix et d’acquisition d’informations afin d’assister l’opérateur à distance, voir même s’y substituer. Pour ce projet, l’architecture de contrôle HBBA (Hybrid Behavior-Based Architecture) sert de pilier pour unifier les bibliothèques requises, RTAB-Map (Real-Time Appearance-Based Mapping) et ODAS (Open embeddeD Audition System), pour réaliser cette intégration. RTAB-Map est une bibliothèque permettant la localisation et la cartographie simultanée selon différentes configurations de capteurs tout en respectant les contraintes de traitement en ligne. ODAS est une bibliothèque permettant la localisation, le suivi et la séparation de sources sonores en milieux réels. Les objectifs sont d’évaluer ces capacités en environnement réel en déployant la plateforme robotique dans différents domiciles, et d’évaluer le potentiel d’une telle intégration en réalisant un scénario autonome d’assistance à la prise de mesure de signes vitaux. La plateforme robotique Beam+ est utilisée pour réaliser cette intégration. La plateforme est bonifiée par l’ajout d’une caméra RBG-D, d’une matrice de huit microphones, d’un ordinateur et de batteries supplémentaires. L’implémentation résultante, nommée SAM, a été évaluée dans 10 domiciles pour caractériser la navigation et le suivi de conversation. Les résultats de la navigation suggèrent que les capacités de navigation fonctionnent selon certaines contraintes propres au positionement des capteurs et des conditions environnementales, impliquant la nécessité d’intervention de l’opérateur pour compenser. La modalité de suivi de la voix fonctionne bien dans des environnements calmes, mais des améliorations sont requises en milieu bruyant. Incidemment, la réalisation d’un scénario d’assistance complètement autonome est fonction des performances de la combinaison de ces fonctionnalités, ce qui rend difficile d’envisager le retrait complet d’un opérateur dans la boucle de décision. L’intégration des modalités avec HBBA s’avère possible et concluante, et ouvre la porte à la réutilisabilité de l’implémentation sur d’autres plateformes robotiques qui pourraient venir compenser face aux lacunes observées sur la mise en œuvre avec la plateforme Beam+

Meetings and Meeting Modeling in Smart Environments

In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear

How Does Experience Modulate Auditory Spatial Processing in Individuals with Blindness?

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Investigating Visual to Auditory Crossmodal Compensation in a Model For Acute Blindness

This study examined neural integration of the sensory modalities of vision and hearing. The objective is to investigate whether an effect of cross-modal compensation of visual to auditory networks in human participants occurs with the deprivation of visual input. This model for acute blindness had a novel design that attempted to imitate true blindness. The experiment involved 10 participants wearing opaque contact lenses that blocked visual feedback for a total of five hours. The duration of the total experiment was approximately eight hours, and involved seven sessions. The overall accuracy across time did not improve in blind individuals (p = 0.586), however, there was a significant finding in speaker accuracy (p<0.000), and a significant interaction between session and speaker (p=0.004). Reaction time generated a main effect of session (p<0.000) and a significant main effect of speaker (p<0.000), but no significant interaction between session and speaker with respect to reaction time

ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-92-J-0225); Office of Naval Research (N00014-01-1-0624); Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-00530); American Society of Engineering Educatio

ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-92-J-0225); Office of Naval Research (N00014-01-1-0624); Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-00530); American Society of Engineering Educatio

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load