Remote Heart Rate Sensing and Projection to Renew Traditional Board Games and Foster Social Interactions

International audienceWhile physiological sensors enter the mass market and reach the general public, they are still mainly employed to monitor health – whether it is for medical purpose or sports. We describe an application that uses heart rate feedback as an incentive for social interactions. A traditional board game has been "augmented" through remote physiological sensing, using webcams. Projection helped to conceal the technological aspects from users. We detail how players reacted – stressful situations could emerge when users are deprived from their own signals – and we give directions for game designers to integrate physiological sensors

Video pulse rate variability analysis in stationary and motion conditions

Background: In the last few years, some studies have measured heart rate (HR) or heart rate variability (HRV) parameters using a video camera. This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. To date, most of these works have obtained HRV parameters in stationary conditions, and there are practically no studies that obtain these parameters in motion scenarios and by conducting an in-depth statistical analysis. Methods: In this study, a video pulse rate variability (PRV) analysis is conducted by measuring the pulse-to-pulse (PP) intervals in stationary and motion conditions. Firstly, given the importance of the sampling rate in a PRV analysis and the low frame rate of commercial cameras, we carried out an analysis of two models to evaluate their performance in the measurements. We propose a selective tracking method using the Viola–Jones and KLT algorithms, with the aim of carrying out a robust video PRV analysis in stationary and motion conditions. Data and results of the proposed method are contrasted with those reported in the state of the art. Results: The webcam achieved better results in the performance analysis of video cameras. In stationary conditions, high correlation values were obtained in PRV parameters with results above 0.9. The PP time series achieved an RMSE (mean ± standard deviation) of 19.45 ± 5.52 ms (1.70 ± 0.75 bpm). In the motion analysis, most of the PRV parameters also achieved good correlation results, but with lower values as regards stationary conditions. The PP time series presented an RMSE of 21.56 ± 6.41 ms (1.79 ± 0.63 bpm). Conclusions: The statistical analysis showed good agreement between the reference system and the proposed method. In stationary conditions, the results of PRV parameters were improved by our method in comparison with data reported in related works. An overall comparative analysis of PRV parameters in motion conditions was more limited due to the lack of studies or studies containing insufficient data analysis. Based on the results, the proposed method could provide a low-cost, contactless and reliable alternative for measuring HR or PRV parameters in non-clinical environments.Peer ReviewedPostprint (published version

A wavelet-based decomposition method for a robust extraction of pulse rate from video recordings

Background Remote photoplethysmography (rPPG) is a promising optical method for non-contact assessment of pulse rate (PR) from video recordings. In order to implement the method in real-time applications, it is necessary for the rPPG algorithms to be capable of eliminating as many distortions from the pulse signal as possible. Methods In order to increase the degrees-of-freedom of the distortion elimination, the dimensionality of the RGB video signals is increased by the wavelet transform decomposition using the generalized Morse wavelet. The proposed Continuous-Wavelet-Transform-based Sub-Band rPPG method (SB-CWT) is evaluated on the 101 publicly available RGB facial video recordings and corresponding reference blood volume pulse (BVP) signals taken from the MMSE-HR database. The performance of the SB-CWT is compared with the performance of the state-of-the-art Sub-band rPPG (SB). Results Median signal-to-noise ratio (SNR) for the proposed SB-CWT ranges from 6.63 to 10.39 dB and for the SB from 4.23 to 6.24 dB. The agreement between the estimated PRs from rPPG pulse signals and the reference signals in terms of the coefficients of determination ranges from 0.81 to 0.91 for SB-CWT and from 0.41 to 0.47 for SB. All the correlation coefficients are statistically significant (p < 0.001). The Bland–Altman plots show that mean difference range from 5.37 to 1.82 BPM for SB-CWT and from 22.18 to 18.80 BPM for SB. Discussion The results show that the proposed SB-CWT outperforms SB in terms of SNR and the agreement between the estimated PRs from RGB video signals and PRs from the reference BVP signals

Cardiovascular assessment by imaging photoplethysmography – a review

AbstractOver the last few years, the contactless acquisition of cardiovascular parameters using cameras has gained immense attention. The technique provides an optical means to acquire cardiovascular information in a very convenient way. This review provides an overview on the technique’s background and current realizations. Besides giving detailed information on the most widespread application of the technique, namely the contactless acquisition of heart rate, we outline further concepts and we critically discuss the current state.</jats:p

Sistema de reconhecimento de expressões faciais para deteção de stress

Stress is the body's natural reaction to external and internal stimuli. Despite being something natural, prolonged exposure to stressors can contribute to serious health problems. These reactions are reflected not only physiologically, but also psychologically, translating into emotions and facial expressions. Once this relationship between the experience of stressful situations and the demonstration of certain emotions in response was understood, it was decided to develop a system capable of classifying facial expressions and thereby creating a stress detector. The proposed solution consists of two main blocks. A convolutional neural network capable of classifying facial expressions, and an application that uses this model to classify real-time images of the user's face and thereby verify whether or not it shows signs of stress. The application consists in capturing real-time images from the webcam, extract the user's face, classify which facial expression he expresses, and with these classifications assess whether or not he shows signs of stress in a given time interval. As soon as the application determines the presence of signs of stress, it notifies the user. For the creation of the classification model, was used transfer learning, together with finetuning. In this way, we took advantage of the pre-trained networks VGG16, VGG19, and Inception-ResNet V2 to solve the problem at hand. For the transfer learning process, were also tried two classifier architectures. After several experiments, it was determined that VGG16, together with a classifier made up of a convolutional layer, was the candidate with the best performance at classifying stressful emotions. Having presented an MCC of 0.8969 in the test images of the KDEF dataset, 0.5551 in the Net Images dataset, and 0.4250 in the CK +.O stress é uma reação natural do corpo a estímulos externos e internos. Apesar de ser algo natural, a exposição prolongada a stressors pode contribuir para sérios problemas de saúde. Essas reações refletem-se não só fisiologicamente, mas também psicologicamente. Traduzindose em emoções e expressões faciais. Uma vez compreendida esta relação entre a experiência de situações stressantes e a demonstração de determinadas emoções como resposta, decidiu-se desenvolver um sistema capaz de classificar expressões faciais e com isso criar um detetor de stress. A solução proposta é constituida por dois blocos fundamentais. Uma rede neuronal convolucional capaz de classificar expressões faciais e uma aplicação que utiliza esse modelo para classificar imagens em tempo real do rosto do utilizador e assim averiguar se este apresenta ou não sinais de stress. A aplicação consiste em captar imagens em tempo real a partir da webcam, extrair o rosto do utilizador, classificar qual a expressão facial que este manifesta, e com essas classificações avaliar se num determinado intervalo temporal este apresenta ou não sinais de stress. Assim que a aplicação determine a presença de sinais de stress, esta irá notificar o utilizador. Para a criação do modelo de classificação, foi utilizado transfer learning, juntamente com finetuning. Desta forma tirou-se partido das redes pre-treinadas VGG16, VGG19, e InceptionResNet V2 para a resolução do problema em mãos. Para o processo de transfer learning foram também experimentadas duas arquiteturas de classificadores. Após várias experiências, determinou-se que a VGG16, juntamente com um classificador constituido por uma camada convolucional era a candidata com melhor desempenho a classificar emoções stressantes. Tendo apresentado um MCC de 0,8969 nas imagens de teste do conjunto de dados KDEF, 0,5551 no conjunto de dados Net Images, e 0,4250 no CK+

Methods for acquisition and integration of personal wellness parameters

Wellness indicates the state or condition of being in good physical and mental health. Stress is a common state of emotional strain that plays a crucial role in the everyday quality of life. Nowadays, there is a growing individual awareness of the importance of a proper lifestyle and a generalized trend to become an active part in monitoring, preserving, and improving personal wellness for both physical and emotional aspects. The majority studies in this field relies on the evaluation of the changes of sensed parameters passing from rest to “maximal” stress. However, the vast majority of people usually experiences stressing circumstances in everyday life. This led us to investigate the impact of mild cognitive activation which can be somehow comparable to usual situations that everyone can face in daily life. Several signals and data can be useful to characterize the state of a person, but not all of them are equally important. So it is crucial to analyse the mutual relevance of the different pieces of information. In this work we focus on a subset of well-established psychophysical descriptors and we identified a set of devices enabling the measurement of these parameters . The design of the experimental setup and the selection of sensing devices were driven by qualitative criteria such as intrusiveness, reliability, and ease of use. These are deemed crucial for implementing effective (self-)monitoring strategies. A reference dataset, named “Mild Cognitive Activation” (MCA), was collected. The last aim of the project was the definition of a quantitative model for data integration providing a concise description of the wellness status of a person. This process was based on unsupervised learning paradigms. Data from MCA were integrated with data from the “Stress Recognition in Automobile Drivers” dataset . This allowed a cross validation of the integration methodology