Smart mirrors are devices that can display any kind of information and can interact with the user using touch and voice commands. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters' emulation and machine learning predictive capabilities.

To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein-Uhlenbeck process its parameters are then exploited to infer potential causal effects in the attribution of social traits. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. This practice brings up hitherto overlooked intricacies. A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods.

Magic mirror software