Reconnaissance des expressions faciales pour l’assistance ambiante

Au cours de ces dernières décennies, le monde a connu d’importants changements démographiques et notamment au niveau de la population âgée qui a fortement augmenté. La prise d’âge a comme conséquence directe non seulement une perte progressive des facultés cognitives, mais aussi un risque plus élevé d’être atteint de maladies neurodégénératives telles qu’Alzheimer et Parkinson. La perte des facultés cognitives cause une diminution de l’autonomie et par conséquent, une assistance quotidienne doit être fournie à ces individus afin d’assurer leur bien-être. Les établissements ainsi que le personnel spécialisé censés les prendre en charge représentent un lourd fardeau pour l’économie. Pour cette raison, d’autres solutions moins coûteuses et plus optimisées doivent être proposées. Avec l’avènement des nouvelles technologies de l’information et de la communication, il est devenu de plus en plus aisé de développer des solutions permettant de fournir une assistance adéquate aux personnes souffrant de déficiences cognitives. Les maisons intelligentes représentent l’une des solutions les plus répandues. Elles exploitent différents types de capteurs pour la collecte de données, des algorithmes et méthodes d’apprentissage automatique pour l’extraction/traitement de l’information et des actionneurs pour le déclenchement d’une réponse fournissant une assistance adéquate. Parmi les différentes sources de données qui sont exploitées, les images/vidéos restent les plus riches en termes de quantité. Les données récoltées permettent non seulement la reconnaissance d’activités, mais aussi la détection d’erreur durant l’exécution de tâches/activités de la vie quotidienne. La reconnaissance automatique des émotions trouve de nombreuses applications dans notre vie quotidienne telles que l’interaction homme-machine, l’éducation, la sécurité, le divertissement, la vision robotique et l’assistance ambiante. Cependant, les émotions restent un sujet assez complexe à cerner et de nombreuses études en psychologie et sciences cognitives continuent d’être effectuées. Les résultats obtenus servent de base afin de développer des approches plus efficaces. Les émotions humaines peuvent être perçues à travers différentes modalités telle que la voix, la posture, la gestuelle et les expressions faciales. En se basant sur les travaux de Mehrabian, les expressions faciales représentent la modalité la plus pertinente pour la reconnaissance automatique des émotions. Ainsi, l’un des objectifs de ce travail de recherche consistera à proposer des méthodes permettant l’identification des six émotions de base à savoir : la joie, la peur, la colère, la surprise, le dégoût et la tristesse. Les méthodes proposées exploitent des données d’entrée statiques et dynamiques, elles se basent aussi sur différents types de descripteurs/représentations (géométrique, apparence et hybride). Après avoir évalué les performances des méthodes proposées avec des bases de données benchmark à savoir : JAFFE, KDEF, RaFD, CK+, MMI et MUG. L’objectif principal de ce travail de recherche réside dans l’utilisation des expressions faciales afin d’améliorer les performances des systèmes d’assistance existants. Ainsi, des expérimentations ont été conduites au sein de l’environnement intelligent LIARA afin de collecter des données de validation, et ce, en suivant un protocole d’expérimentation spécifique. Lors de l’exécution d’une tâche de la vie quotidienne (préparation du café), deux types de données ont été récoltés. Les données RFID ont permis de valider la méthode de reconnaissance automatique des actions utilisateurs ainsi que la détection automatique d’erreurs. Quant aux données faciales, elles ont permis d’évaluer la contribution des expressions faciales afin d’améliorer les performances du système d’assistance en termes de détection d’erreurs. Avec une réduction du taux de fausses détections dépassant les 20%, l’objectif fixé a été atteint avec succè

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System

At the present time, various freely available or commercial solutions are used to classify the subject's emotional state. Classification of the emotional state helps us to understand how the subject feels and what he is experiencing in a particular situation. Classification of the emotional state can thus be used in various areas of our life from neuromarketing, through the automotive industry (determining how emotions affect driving), to implementing such a system into the learning process. The learning process, which is the (mutual) interaction between the teacher and the learner, is an interesting area in which individual emotional states can be explored. In this pedagogical-psychological area several research studies were realized. These studies in some cases demonstrated the important impact of the emotional state on the results of the students. However, for comparison and unambiguous classification of the emotional state most of these studies used the instructed (even constructed) stereotypical facial expressions of the most well-known test databases (Jaffe is a typical example). Such facial expressions are highly standardized, and the software can recognize them with a fairly big percentage, but this does not necessarily point to the actual success rate of the subject's emotional classification in such a test because the similarity to real emotional expression remains unknown. Therefore, we examined facial expressions in real situations. We have subsequently compared these examined facial expressions with the instructed expressions of the same emotions (the Jaffe database). The overall average classification score in real facial expressions was 94.58%

A High-Fidelity Open Embodied Avatar with Lip Syncing and Expression Capabilities

Embodied avatars as virtual agents have many applications and provide benefits over disembodied agents, allowing non-verbal social and interactional cues to be leveraged, in a similar manner to how humans interact with each other. We present an open embodied avatar built upon the Unreal Engine that can be controlled via a simple python programming interface. The avatar has lip syncing (phoneme control), head gesture and facial expression (using either facial action units or cardinal emotion categories) capabilities. We release code and models to illustrate how the avatar can be controlled like a puppet or used to create a simple conversational agent using public application programming interfaces (APIs). GITHUB link: https://github.com/danmcduff/AvatarSimComment: International Conference on Multimodal Interaction (ICMI 2019

Modeling the user state for context-aware spoken interaction in ambient assisted living

Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfill these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02- 02, CAM CONTEXTS (S2009/TIC-1485

Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer