Smart environment architecture for emotion detection and regulation

Abstract

This paper introduces an architecture as a proof-of-concept for emotion detection and regulation in smart health environments. The aim of the proposal is to detect the patient's emotional state by analysing his/her physiological signals, facial expression and behaviour. Then, the system provides the best-tailored actions in the environment to regulate these emotions towards a positive mood when possible. The current state-of-the-art in emotion regulation through music and colour/light is implemented with the final goal of enhancing the quality of life and care of the subject. The paper describes the three main parts of the architecture, namely "Emotion Detection", "Emotion Regulation" and "Emotion Feedback Control". "Emotion Detection" works with the data captured from the patient, whereas "Emotion Regulation" offers him/her different musical pieces and colour/light settings. "Emotion Feedback Control" performs as a feedback control loop to assess the effect of emotion regulation over emotion detection. We are currently testing the overall architecture and the intervention in real environments to achieve our final goal.

A performance comparison of two emotion-recognition implementations using OpenCV and Cognitive Services API

“…Then, the total mean, P, and the mean of class i, P i where i {1, 2, …, c} are computed as described in equations (1) and (2). These values are used in equations (3) and (4) (4) A projection W maximizes the class separability criterion by following equation (5): (5) The General Eigenvalue Problem solves this optimization task (6): (6) The rank of the scatter matrix, S w , is at most (N samples -c classes).…”

“…Facial emotion recognition seeks to predict the real feeling that a person expresses based on facial images, with a wide range of possible applications, such as improving student engagement [1], building smart health environments [2], analyzing customers' feedback [3] and evaluating the quality of children's games [4], just to name a few.…”

Estimation of Mental Distress from Photoplethysmography

“…Negative stress (or distress) is one of the most important mental states due to its significant effects in health [1][2][3]. Distress is considered cause and consequence of failure and difficulties in a wide variety of daily situations.…”

“…Mental distress can result in negative views of the environment, others, and the self. This is why it is important to investigate on devices and environments capable of recognizing and/or regulating negative emotions [1][2][3][4][5]. Sadness, anxiety, distraction, and symptoms of mental illness are manifestations of psychological distress.…”

Emotion Recognition in Video with OpenCV and Cognitive Services API: A Comparison

“…Potential applications of emotion recognition include the improvement of student engagement [1], the built of smart health environments [2], the analysis of customers' feedback [3], and the evaluation of quality in children's games [4], among others. Face recognition within multimedia elements, such as images and videos, has been one of the challenges in the artificial intelligence field.…”