Michela Ferron

Gianluca Schiavo, Ornella Mich, Michela Ferron et al.

This paper presents key aspects and trade-offs that designers and Human-Computer Interaction practitioners might encounter when designing multimodal interaction for older adults. The paper gathers literature on multimodal interaction and assistive technology, and describes a set of design challenges specific for older users. Building on these main design challenges, four trade-offs in the design of multimodal technology for this target group are presented and discussed. To highlight the relevance of the trade-offs in the design process of multimodal technology for older adults, two of the four reported trade-offs are illustrated with two user studies that explored mid-air and speech-based interaction with a tablet device. The first study investigates the design trade-offs related to redundant multimodal commands in older, middle-aged and younger adults, whereas the second one investigates the design choices related to the definition of a set of mid-air one-hand gestures and voice input commands. Further reflections highlight the design trade-offs that such considerations bring in the process, presenting an overview of the design choices involved and of their potential consequences.

Andrey Bogomolov, Bruno Lepri, Michela Ferron et al.

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.