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Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to grasp how engagement is naturally expressed through behavior or too complex to be traced back to an overall engagement state. We carried out a longitudinal study to develop a coding system of engagement-related behavior that could tackle these issues and to create an evidence-based model of engagement to make meaning of such a coding system. Fourteen elderlies with mild to moderate dementia took part in the study. They were involved in two activities: a game-based cognitive stimulation and a robot-based free play. The coding system was developed with a mixed approach: ethographic and Laban-inspired. First, we developed two ethograms to describe the behavior of participants in the two activities in detail. Then, we used Laban Movement Analysis (LMA) to identify a common structure to the behaviors in the two ethograms and unify them in a unique coding system. The inter-rater reliability (IRR) of the coding system proved to be excellent for cognitive games (kappa = 0.78) and very good for robot play (kappa = 0.74). From the scoring of the videos, we developed an evidence-based model of engagement. This was based on the most frequent patterns of body part organization (i.e., the way body parts are connected in movement) observed during activities. Each pattern was given a meaning in terms of engagement by making reference to the literature. The model was tested using structural equation modeling (SEM). It achieved an excellent goodness of fit and all the hypothesized relations between variables were significant. We called the coding system that we developed the Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the model the Evidence-based Model of Engagement-related Behavior (EMODEB). To the best of our knowledge, the ELICSE and the EMODEB constitute the first formalization of engagement-related behavior for dementia that describes how behavior unfolds over time and what it means in terms of engagement.

Engagement in activities is crucial to improve quality of life in dementia. Yet, its measurement relies exclusively on behavior observation and the influence that behavioral and psychological symptoms of dementia (BPSD) have on it is overlooked. This study investigated whether quantity of movement, gauged with a wrist-worn accelerometer, could be a sound measure of engagement and whether apathy and depression negatively affected engagement. Fourteen participants with dementia took part in 6 sessions of activities: 3 of cognitive games (eg, jigsaw puzzles) and 3 of robot play (Pleo). Results highlighted significant correlations between quantity of movement and observational scales of engagement and a strong negative influence of apathy and depression on engagement. Overall, these findings suggest that quantity of movement could be used as an ancillary measure of engagement and underline the need to profile people with dementia according to their concurrent BPSD to better understand their engagement in activities.

In this paper, we report an experimental study designed to examine how participants perceive and interpret social hints from gaze exhibited by either a robot or a human tutor when carrying out a matching task. The underlying notion is that knowing where an agent is looking at provides cues that can direct attention to an object of interest during the activity. In this regard, we asked human participants to play a card matching game in the presence of either a human or a robotic tutor under two conditions. In one case, the tutor gave hints to help the participant find the matching cards by gazing toward the correct match, in the other case, the tutor only looked at the participants and did not give them any help. The performance was measured based on the time and the number of tries taken to complete the game. Results show that gaze hints (helping tutor) made the matching task significantly easier (fewer tries) with the robot tutor. Furthermore, we found out that the robots' gaze hints were recognized significantly more often than the human tutor gaze hints, and consequently, the participants performed significantly better with the robot tutor. The reported study provides new findings towards the use of non-verbal gaze hints in human-robot interaction, and lays out new design implications, especially for robot-based educative interventions., Competing Interests: Written consent was acquired from each participant prior to the experimental sessions. This was a non-clinical study without any harming procedure and all data were collected anonymously. Therefore, according to the Netherlands Code of Conduct for Scientific Practice (principle 1.2 on page 5), ethical approval was not sought for execution of this study.The authors declare that they have no conflict of interest.

In this paper, we present a novel tool to measure engagement in people with dementia playing board games and interacting with a social robot, Pleo. We carried out two studies to reach a comprehensive inventory of behaviours accounting for engagement in dementia. The first one is an exploratory study aimed at modelling engagement in cognitive board games. The second one is a longitudinal study to investigate how people with dementia express engagement in cognitive games and in interactions with social robots. We adopted a technique coming from Ethology to mould behaviour, the ethogram. Ethogram is founded on low level behaviours, and allows hierarchical structuring. Herein, we present preliminary results consisting in the description of two ethograms and in their structuring obtained through thematic analysis. Such results show that an underlying structure of engagement exists across activities, and that different activities trigger different behavioural displays of engagement that adhere to such a structure.

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds.

Unconscious mental processes have recently started gaining attention in a number of scientific disciplines. One of the theoretical frameworks for describing unconscious processes was introduced by Jung as a part of his model of the psyche. This framework uses the concept of archetypes that represent prototypical experiences associated with objects, people, and situations. Although the validity of Jungian model remains an open question, this framework is convenient from the practical point of view. Moreover, archetypes found numerous applications in the areas of psychology and marketing. Therefore, observation of both conscious and unconscious traces related to archetypal experiences seems to be an interesting research endeavor. In a study with 36 subjects, we examined the effects of experiencing conglomerations of unconscious emotions associated with various archetypes on the participants' introspective reports and patterns of physiological activations. Our hypothesis for this experiment was that physiological data may predict archetypes more precisely than introspective reports due to the implicit nature of archetypal experiences. Introspective reports were collected using the Self-Assessment Manikin (SAM) technique. Physiological measures included cardiovascular, electrodermal, respiratory responses and skin temperature of the subjects. The subjects were stimulated to feel four archetypal experiences and four explicit emotions by means of film clips. The data related to the explicit emotions served as a reference in analysis of archetypal experiences. Our findings indicated that while prediction models trained on the collected physiological data could recognize the archetypal experiences with accuracy of 55 percent, similar models built based on the SAM data demonstrated performance of only 33 percent. Statistical tests enabled us to confirm that physiological observations are better suited for observation of implicit psychological constructs like archetypes than introspective reports.

This work proposes a concept for indoor ambulatory monitoring for Parkinson's disease patients. In the proposed concept, a wearable inertial sensor is kept as the main monitoring device through the day, and it is expanded by an ambient sensor system in the specific living areas with high estimated probability of occurrence of freezing of gait episode. The ambient sensor system supports decisions of the wearable sensor system by providing relevant spatial context information of the user, which is obtained through precise localization.

To support the human factors engineer in designing a good user interface, a method has been developed to analyse the empirical data of the interactive user behaviour traced in a finite discrete state space. The sequences of actions produced by the user contain valuable information about the mental model of this user, the individual problem solution strategies for a given task and the hierarchical structure of the task-subtasks relationships. The presented method, AMME, can analyse the action sequences and automatically generate (1) a net description of the task dependent model of the user, (2) a complete state transition matrix, and (3) various quantitative measures of the user's task solving process. The behavioural complexity of task-solving processes carried out by novices has been found to be significantly larger than the complexity of task-solving processes carried out by experts.