Yvonne Delevoye

Professeure des universités - Psychologie Cognitive
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Yvonne Delevoye

Professeure des universités - Psychologie Cognitive

Axes de recherche

Game urban design to motivate citizens to engage in physical activities

There is an increasing interest and experimentation regarding the notion of a playful city in Europe and in the United States .Several projects have initiated gamifications of cities, with the aim to promote a more active lifestyle (Donoff & Bridgman, 2017). Gamification can be described as the application of game or play elements and digital game design techniques to non-game problems, such as business and social impact challenges (Deterding, Dixon, Khaled, & Nacke, s.d.). In other words, gamification is a way of improving people’s efficiency and engagement in a variety of life sectors (e.g. work, marketing, health, education etc.), by employing game design elements. For the purpose of this research, the gamification will correspond to the different motivation profiles of human adult participants (Delevoye-Turrell, Batistatou,& Deplancke, 2018). All the activities can be performed at different levels of intensity, easily modified by each participant to correspond to the need of the moment. With this ludic installation, our goal is to create playful pathways within the city that promote physical activity and give the desire to all individuals to engage in body movement - whatever the age ,gender and fitness level

Spontaneous motor tempo and positiveaffective designs to engage citizens to be active

There is no engagement to physical activity without pleasure  (Van Cappellen, Rice, Catalino & Fredrickson,   2018) and the frequency at which an individual is performing a movement may be an important determinant of the experienced pleasure during motor activity (Parfitt, Evans  &  Eston,  2012). Delevoye-Turrell, Dione   and Agneray (2014) reported a motor facilitation effect in both a spatial-tapping task and a cycling task for tempi close to 2 Hz. In addition to be experienced as more pleasurable, both tasks were actually accomplished with better stability and greater accuracy. In the cognitive literature of motor timing, the 2 Hz frequency has been referred to as the motor signature of spontaneous tempo, i.e., the most natural tempo at which an individual choses to move (Fraisse, Chambron, &Oléron, 1954; Fraisse, 1974). Interestingly, the spontaneous motor tempo was also found to be the frequency at which performing an action requires the least cognitive   resources   (Guérin,   Boitout,  & Delevoye-Turrell,  2021). Thus, thisbparticular tempo could be the one for which the attentional focus is the mostfrequently directed towards the outside world. Such focus would promote a state of social openness on which gamified environments could be used to trigger the emergence of pleasurable experiences and active engagement towards physical exercise. Nowadays, it is known that the spontaneous motor tempo may vary according to different environmental factors, and in particular emotional cues (Monier & Droit-Volet, 2016). In the present project, sensorial design (shapes, colors, odors,sounds) will be used as emotional stimuli to induce particular emotional states inhuman participants in order to provide them with the opportunity to discover pleasurable physical activity. The fNIRS technique is used to quantify the changes in different brain areas (Guérin et al., 2021).

Machine Learning to predict the impact of sensorial design on daily living decisions

In contemporary design, human body is becoming an important design material. Researches have focused on human body as an interface (Hansen, 2012) and with the fast-moving new technologies, human bodies become the tools for interaction within smart cities. Nevertheless, knowledge-based research is now required to explore the possibilities of the emotional body and testing new interactive designs to encourage physical activity in the urban environment. 

Humans have the remarkable ability to recognize through observation of facial expressions the inner state of emotion of a conspecific (Ekman, 1965). This ability stems from the fact that our emotions are embedded within our own body postures and movements (Pollick, Paterson, Bruderlin, & Sanford, 2001). Recently, Wamain, Demay, and Delevoye-Turrell (2016) used 3D motion capture to demonstrate that naïve observers are able to classify emotions based on motion kinematics even when (1) facial expressions were masked and (2) when emotions were induced implicitly through the use of emotional sensorial environments. In collaboration with computer scientists, automatically classifiers demonstrated the ability to automatically categorize emotional states on the basis of 3D body posture and dynamics even on constrained movements (e.g., walking down a path - Daoudi, Berretti, Pala, Delevoye-Turrell, & Del Bimbo, 2017). The algorithm reached a percentage of accuracy close to that performed by human classifiers (~75%) both for walking (Delevoye-Turrell et al., submitted) and for cycling (Brossard & Delevoye-Turrell, in preparation). The objective of the future work will be to develop the automatic classifiers for outdoor activities in individuals who are freely discovering playful installations in the city.


But discovering is not sufficient for regular engagement. Hence, the second target of our work will be to develop machine learning algorithms to offer adaptive environments. The starting point of such a project is to adapt tempo-selected music to the exerciser’s self-paced movements in order to increase experience of motivation and pleasure. The extended work will aim to include real-time detection of emotional and motivational aspects of exercisers in the real-world.