Using social robots to study abnormal social development

Social robots recognize and respond to human social cues with appropriate behaviors. Social robots, and the technology used in their construction, can be unique tools in the study of abnormal social development. Autism is a pervasive developmental disorder that is characterized by social and communicative impairments. Based on three years of integration and immersion with a clinical research group which performs more than 130 diagnostic evaluations of children for autism per year, this paper discusses how social robots will make an impact on the ways in which we diagnose, treat, and understand autism

A study of virtual reality-mediated affective state and cognitive decline in Alzheimer’s disease

NeuroscienceLa démence de type d’Alzheimer est la plus commune des démences. Elle entraîne un déclin dans les capacités cognitives et fonctionnelles, se traduisant dans des difficultés au niveau de la prise de décision, de l’accomplissement de tâches quotidiennes, de la communication ainsi qu’au niveau de la mémoire et de l’attention. On remarque également une diminution de l’état émotionnel et une apathie chez ces patients. Ce mémoire explore une nouvelle approche pour atténuer les effets psychologiques et cognitifs de la maladie. Les recherches effectuées dans ce mémoire explorent les impacts cognitifs et les effets sur le bien-être d'une intervention utilisant la réalité virtuelle sur les personnes souffrant de déclin cognitif subjectif. Deux environnements virtuels ont été testés : le premier étant un environnement dans lequel le participant voyage en train à travers différents climats, et le second étant un environnement de musicothérapie qui s’adapte en fonction de la réponse émotionnelle du participant. Pour mesurer les impacts sur l'état affectif, des lectures électroencéphalographiques ont été prises et analysées afin de déduire l'émotion ressentie par le participant avant, pendant et après l'expérience. Les résultats montrent une amélioration générale de l'état émotionnel pour les deux environnements. Quant à la mesure des effets sur les fonctions cognitives, des tâches d'attention et de mémoire ont été effectuées par les participants avant et après l'immersion. Les résultats montrent une légère amélioration des capacités d'attention et une meilleure amélioration de la mémoire. Nous approprions cet écart dans l'expérience de musicothérapie à l'activation musicale d'un réseau de structures cérébrales impliquées dans les expériences agréables : le circuit de récompense. Nous proposons que la musique facilite la rétention de la mémoire chez les personnes souffrant de démence. En effet, les résultats de l’amélioration des fonctions cognitives pour les deux expériences précédentes dépendent fortement de la précision de l'outil de mesure cognitive utilisé pour évaluer les performances d'attention et de mémoire avant et après l'intervention. Pour assurer cette précision, ce mémoire présente un outil de mesure des performances cognitives basé sur des tâches cognitives qui ont montré à plusieurs reprises leur fiabilité. Cet outil d’adresse aux personnes atteintes de la maladie d'Alzheimer pré-clinique et diagnostiquée.Alzheimer’s disease is an irreversible disease which causes progressive memory loss and cognitive decline, eventually leading to severe inability to perform basic day-to-day tasks. The urgency to find an effective cure to the disease is crucial, as the medical and economical spin-offs could be disastrous. The present thesis explores a novel approach to help attenuate the psychological and cognitive effects of the disease. The research carried out for this thesis explored cognitive effects and impacts on overall well-being of a virtual reality intervention on people suffering from subjective cognitive decline. Two virtual environments were tested: the first being an environment in which the participant travels through different climates by train, and the second being a music therapy environment modified as a function of emotional response. To measure the effects on affective state, electroencephalography readings were taken and analyzed to infer the emotion felt by the participant before, during the experiment. Results show a general improvement in emotional state. To measure the effects of the environments on cognitive functions, attention and memory tasks were carried out by the participants before and after the immersion. Results show a small improvement in attention skills and a more substantial improvement in memory skills. We appropriate this discrepancy in the music therapy experiment to the musical activation of a network of brain structures involved in rewarding and pleasurable experiences. We propose that music could facilitate memory retention in people sufferance for dementia. Importantly, the results of the previous experiments rely heavily on the accuracy of the cognitive measurement tool used to evaluate attention and memory performances before and after the intervention. To provide this accuracy, this thesis presents a cognitive performance measurement tool based on cognitive tasks which have repeatedly shown to output reliable results. This tool is created to serve for people with pre-clinical Alzheimer’s disease and diagnosed Alzheimer’s disease. Additionally, this tool is designed in such a way as to minimize the effects of repetition as well as varying levels of education and language. This thesis presents a novel and promising research in the realms of computer sciences and health care

Waiting for a digital therapist : three challenges on the path to psychotherapy delivered by artificial intelligence

Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called “general” or “human-like” AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy

Current Understanding, Support Systems, and Technology-led Interventions for Specific Learning Difficulties

In January 2019, the Government Office for Science commissioned a series of 4 rapid evidence reviews to explore how technology and research can help improve educational outcomes for learners with Specific Learning Difficulties (SpLDs). This review examined: 1) current understanding of the causes and identification of SpLDs, 2)the support system for learners with SpLDs, 3)technology-based interventions for SpLDs 4) a case study approach focusing on dyscalculia to explore all 3 theme

A semantic memory bank assisted by an embodied conversational agents for mobile devices

Alzheimer’s disease is a type of dementia that causes memory loss and interferes with intellectual abilities seriously. It has no current cure and therapeutic efficiency of current medication is limited. However, there is evidence that non-pharmacological treatments could be useful to stimulate cognitive abilities. In the last few year, several studies have focused on describing and under- standing how Virtual Coaches (VC) could be key drivers for health promotion in home care settings. The use of VC gains an augmented attention in the considerations of medical innovations. In this paper, we propose an approach that exploits semantic technologies and Embodied Conversational Agents to help patients training cognitive abilities using mobile devices. In this work, semantic technologies are used to provide knowledge about the memory of a specific person, who exploits the structured data stored in a linked data repository and take advantage of the flexibility provided by ontologies to define search domains and expand the agent’s capabilities. Our Memory Bank Embodied Conversational Agent (MBECA) is used to interact with the patient and ease the interaction with new devices. The framework is oriented to Alzheimer’s patients, caregivers, and therapists

Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications