Impact d'un Robot " Majordome " sur l'état psychoaffectif et cognitif de personnes âgées ayant des troubles cognitifs

Les personnes âgées souffrant de troubles cognitifs ont besoin de services, en particulier d'aide sous forme d'entrainement cognitif et de facilitation des contacts sociaux, auxquelles les technologies de l'information et de la communication peuvent répondre. Les équipes (Broca, Valoria, ISIR, Robosoft) du projet TECSAN Robadom (financé par l'ANR) développent et testent auprès de personnes âgées souffrant de troubles cognitifs légers, un robot doté d'émotions et du langage, adapté aux difficultés de ces personnes, contrôlé par elles et qui pourrait contribuer à leur soutien au domicile en apportant différents services tels que des aides matérielles, des relais d'information, un soutien psychologique et cognitif. En début de projet, les scénarii de validations du robot et des spécifications techniques de l'interaction ont été réalisés. La deuxième phase comportait la conception du robot, le développement de la perception multimodale centrée sur l'utilisateur et du modèle émotionnel et cognitif d'interaction. La troisième phase est constituée des évaluations cliniques. Cette tâche permet d'étudier l'acceptabilité, l'utilisabilité et l'impact du robot dans la vie quotidienne (affectif, cognitif, qualité de vie...) et la manière dont le robot est perçu (compagnon, machine, intrus) par les utilisateurs, ainsi que les questions d'éthiques soulevées par le projet à travers une approche transversale prenant en compte aussi bien les dimensions normatives (lois, droits, etc.) que proactives (opinions des usagers)

Le projet Robadom : conception d'un robot d'assistance pour les personnes âgées

National audienceContexte : Le projet ROBADOM a pour objectif de concevoir "un robot majordome", capable de fournirdes interactions verbales et non verbales et des feedbacks pour aider au quotidien les personnes âgées présentant des troubles cognitifs légers. Objectif : Le projet ROBADOM aborde les thématiques suivantes : 1. Le contexte social pour la conception de robots : 1) définir l'apparence du robot et 2) étudier les perceptions et les attitudes des personnes âgées à l'égard d'un robot d'assistance ; 2. Développer les comportements du robot pour créer une interaction "naturelle": 1) des solutions techniques pour un robot expressif, 2) la communication verbale et non verbale entre les personnes âgées et le robot ; 3. Etudier l'acceptabilité du robot chez les personnes âgées ; 4. Etudier l'impact du robot sur les utilisateurs âgés. Méthode : Les quatre études ont impliqué à la fois une méthode qualitative et une méthode expérimentale, réalisées au sein de notre laboratoire "LUSAGE". Résultats et conclusion : Les petits robots avec des traits stylisés ont été appréciés par les participants. Concernant les fonctionnalités, la stimulation cognitive, le rappel de tâches et la localisation d'objets ont été positivement évalués. Bien que les participants jugent le robot utile, ils n'étaient pas encore prêts à l'adopter. De plus, ils ont perçu certaines expressions du robot différemment des personnes jeunes. Ainsi, le système robotisé devra être adapté aux spécificités des personnes âgées. Enfin, nos participants ont soulevé la question de la valeur ajoutée d'un système robotisé par rapport à un ordinateur. Ainsi, de nombreux aspects (technologiques, interaction homme-robot, sociologiques...) restent à explorer avant d'évaluer l'impact du robot d'assistance au domicile

Perception of affects from non-facial expressions of the robot Nabaztag

International audienceSocial robots using language and affective expressions can encourage and improve human-robot interaction. Body move-ments, postures, orientations, colors, and sounds can be used as either the primary method of expres-sion or to provide affective expression redundancy1. This study aimed at investigating how the elderly and the young perceive affects from expressions of Nabaztag, a non anthropomorphic robot with only non facial expression

eHealth interventions for family carers of people with long term illness: A promising approach?

Family carers of people who have long term illness often experience physical and mental health morbidities, and burden. While there is good evidence to suggest that carers benefit from psychosocial interventions, these have primarily been delivered via face-to-face individual or group-formats. eHealth interventions offer a novel, accessible and self-paced approach to care delivery. Whether these are effective for carers' wellbeing has been little explored. This paper reports the first comprehensive systematic review in this area. A total of 78 studies, describing 62 discrete interventions, were identified. Interventions commonly aimed to promote carers' knowledge, self-efficacy, caregiving appraisal, and reduce global health morbidities. Interventions were offered to carers of people with a wide range of long term illness; dementia has been the most researched area, as reported in 40% of studies. Clinical and methodological heterogeneity in interventions precluded meta-analyses, and so data were analysed narratively. The most popular approach has comprised psychoeducational interventions delivered via an enriched online environment with supplementary modes of communication, such as network support with professionals and peers. Overall, carers appreciate the flexibility and self-paced nature of eHealth interventions, with high rates of satisfaction and acceptability. More studies using robust designs are needed to extend the evidence base

The effectiveness of psychosocial and behavioral interventions for informal dementia caregivers:Meta-analyses and meta-regressions

Background:  Many psychosocial and behavioral interventions have been developed for informal dementia caregivers. Because existing meta-analyses only focused on a limited number of interventions and outcomes, how effective these interventions are overall and which interventions components are associated with larger effects has yet to be explored. Objective:  To provide a comprehensive meta-analysis of the effectiveness of psychosocial and behavioral interventions on burden, depression, anxiety, quality of life, stress, and sense of competence in informal dementia caregivers. In addition, we examined if interventions which utilized more sessions and/or were delivered personally (face-to-face) had larger effect sizes. In exploratory meta-regressions, we examined seven additional moderators. Methods:  The protocol was registered with PROSPERO, number CRD42017062555. We systematically searched the literature to identify controlled trials assessing the effect of psychosocial and behavioral interventions on the six outcome measures, for informal dementia caregivers. We performed six random effects meta-analyses, to assess the pooled effect sizes of the interventions. In addition, we performed separate meta-regressions, for each outcome, for each moderator. Results:  The sample consisted of 60 studies. For all outcomes except anxiety, the pooled effects were small and in favor of the intervention group. No moderator was found to systematically predict these effects. There were no indications for publication bias or selection bias based on significance. Conclusion:  Overall, the interventions yield significant (small) effects, independent of intervention characteristics. Future research should explore options to enhance the effectiveness of interventions aimed at assisting informal caregivers

Characterizing early-stage Alzheimer through spatiotemporal dynamics of handwriting

International audienceWe propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike the literature, we model the loop velocity trajectory (full dynamics) in an unsupervised way. Through a temporal clustering based on K-medoids, with Dynamic Time Warping as dissimilarity measure, we uncover clusters that give new insights on the problem. For classification, we consider a Bayesian formalism that aggregates the contributions of the clusters, by probabilistically combining the discriminative power of each. On a dataset consisting of two cognitive profiles, Early-stage Alzheimer Disease and Healthy persons, each comprising 27 persons collected at Broca Hospital in Paris, our classification performance significantly outperforms the state of the art, based on global kinematic feature

Computerized cognitive stimulation and engagement programs in older adults with mild cognitive impairment: comparing feasibility, acceptability, and cognitive and psychosocial effects

Leila Djabelkhir,1,2 Ya-Huei Wu,1,2 Jean-Sébastien Vidal,1 Victoria Cristancho-Lacroix,1,2 Fabienne Marlats,1,2 Hermine Lenoir,1,2 Ariela Carno,1 Anne-Sophie Rigaud1,2 1Department of Clinical Gerontology, Broca Hospital, Public Assistance – Paris Hospitals (AP-HP), 2Research Team 4468, Paris Descartes University, Paris, France Purpose: Mild cognitive impairment (MCI) is associated with a higher risk of dementia and is becoming a topic of interest for pharmacological and nonpharmacological interventions. With advances in technology, computer-based cognitive exercises are increasingly integrated into traditional cognitive interventions, such as cognitive training. Another type of cognitive intervention involving technology use is cognitive engagement, consisting of involving participants in highly motivational and mentally challenging activities, such as learning to use a form of new digital technology. This study examined the feasibility and acceptability of a computerized cognitive stimulation (CCS) program and a computerized cognitive engagement (CCE) program, and then compared their effects in older adults with MCI.Patients and methods: In this randomized study, data from 19 MCI patients were analyzed (n=9 in CCS and n=10 in CCE). The patients attended a group weekly session for a duration of 3 months. Assessments of cognitive and psychosocial variables were conducted at baseline (M0) and at the end of the programs (M3).Results: All of the participants attended the 12 sessions and showed a high level of motivation. Attrition rate was very low (one dropout at M3 assessment). At M3, the CCS participants displayed a significant improvement in part B of the Trail Making Test (TMT-B; p=0.03) and self-esteem (p=0.005), while the CCE participants showed a significant improvement in part A of the Trail Making Test (TMT-A; p=0.007) and a higher level of technology acceptance (p=0.006). The two groups did not differ significantly (p>0.05) in cognitive and psychosocial changes after the intervention. However, medium effect sizes (Cohen’s d=0.56; 95% CI =–0.43:1.55) were found on the free recall, favoring the CCS group, as well as on TMT-A (d=0.51; 95% CI =–0.48:1.49) and technology acceptance (d=–0.65; 95% CI =–1.64:0.34), favoring the CCE group.Conclusion: Both interventions were highly feasible and acceptable and allowed improvement in different aspects of cognitive and psychosocial functioning in MCI subjects. Keywords: cognitive intervention, mild cognitive impairment, tablet computers, technolog

From aging to early-stage Alzheimer's: uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning

International audienceWe present, in this paper, a novel paradigm for assessing Alzheimer's disease and aging by analyzing impairment of handwriting (HW) on tablets, a challenging problem that is still in its infancy. The state of the art is dominated by methods that assume a unique behavioral trend for each cognitive profile or age group, and that extract global kinematic parameters, assessed by standard statistical tests or classification models, for discriminating the neuropathological disorders (Alzheimer's (AD), Mild Cognitive Impairment (MCI)) from Healthy Controls (HC), or HC age groups from each other. Our work tackles these two major limitations as follows. First, instead of considering a unique behavioral pattern for each cognitive profile or age group, we relax this heavy constraint by allowing the emergence of multimodal behavioral patterns. We achieve this by performing semi or unsupervised learning to uncover homogeneous clusters of subjects, and then we analyze how much information these clusters carry on the cognitive profiles (or age groups). Second, instead of relying on global kinematic parameters, mostly consisting of their average, we refine the encoding either by a semi-global parameterization, or by modeling the full dynamics of each parameter, harnessing thereby the rich temporal information inherently characterizing online HW. To illustrate the power of our paradigm, we present three studies, one regarding age, and two regarding Alzheimer's. Thanks to our modeling, we obtain new findings that are the first of their kind on this research field. On aging, unlike previous works reporting only one pattern of HW change with age, our study, based on a semiglobal parametrization scheme, uncovers three major aging HW styles, one specific to aged subjects and two shared with other age groups. On Alzheimer's, a striking finding is revealed: two major clusters are unveiled, one dominated by HC and MCI subjects, and one by MCI and ES-AD, thus revealing that MCI patients have fine motor skills leaning towards either HC's or ES-AD's. Our paper introduces also a new temporal representation learning from HW trajectories that uncovers a rich set of features simultaneously like the full velocity profile, size and slant, fluidity, and shakiness, and reveals, in a naturally explainable way, how these HW features conjointly characterize, with fine and subtle details, the cognitive profiles