7,132 research outputs found
Attention, concentration, and distraction measure using EEG and eye tracking in virtual reality
Attention is important in learning, Attention-deficit/hyperactivity disorder, Driving, and many other fields. Hence, intelligent tutoring systems, Attention-deficit/hyperactivity disorder diagnosis systems, and distraction detection of driver systems should be able to correctly monitor the attention levels of individuals in real time in order to estimate their attentional state. We study the feasibility of detecting distraction and concentration by monitoring participants' attention levels while they complete cognitive tasks using Electroencephalography and Eye Tracking in a virtual reality environment. Furthermore, we investigate the possibility of improving the concentration of participants using relaxation in virtual reality. We developed an indicator that estimates levels of attention with a real value using EEG data. The participant-independent indicator based on EEG data we used to assess the concentration levels of participants correctly predicts the concentration state with an accuracy (F1 = 73%). Furthermore, the participant-independent distraction model based on Eye Tracking data correctly predicted the distraction state of participants with an accuracy (F1 = 89%) in a participant-independent validation setting.La concentration est importante dans lâapprentissage, Le trouble du dĂ©ficit de lâattention avec ou sans hyperactivitĂ©, la conduite automobile et dans de nombreux autres domaines. Par consĂ©quent, les systĂšmes de tutorat intelligents, les systĂšmes de diagnostic
du trouble du dĂ©ficit de lâattention avec ou sans hyperactivitĂ© et les systĂšmes de dĂ©tection de la distraction au volant devraient ĂȘtre capables de surveiller correctement les
niveaux dâattention des individus en temps rĂ©el afin de dĂ©duire correctement leur Ă©tat
attentionnel. Nous Ă©tudions la faisabilitĂ© de la dĂ©tection de la distraction et de la concentration en surveillant les niveaux dâattention des participants pendant quâils effectuent
des tĂąches cognitives en utilisant lâĂlectroencĂ©phalographie et lâEye Tracking dans un
environnement de rĂ©alitĂ© virtuelle. En outre, nous Ă©tudions la possibilitĂ© dâamĂ©liorer la
concentration des participants en utilisant la relaxation en réalité virtuelle. Nous avons
mis au point un indicateur qui estime les niveaux dâattention avec une valeur rĂ©elle en
utilisant les donnĂ©es EEG. Lâindicateur indĂ©pendant du participant basĂ© sur les donnĂ©es
EEG que nous avons utilisé pour évaluer les niveaux de concentration des participants
prĂ©dit correctement lâĂ©tat de concentration avec une prĂ©cision (F1 = 73%). De plus, le
modĂšle de distraction indĂ©pendant des participants, basĂ© sur les donnĂ©es dâEye Tracking,
a correctement prĂ©dit lâĂ©tat de distraction des participants avec une prĂ©cision (F1 = 89%)
dans un cadre de validation indépendant des participants
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
Machine Learning Models for Mental Stress Classification based on Multimodal Biosignal Input
Mental stress is a largely prevalent condition directly or indirectly responsible for
almost half of all work-related diseases. Work-Related Stress is the second most impactful
occupational health problem in Europe, behind musculoskeletal diseases. When mental
health is adequately handled, a workerâs well-being, performance, and productivity can
be considerably improved.
This thesis presents machine learning models to classify mental stress experienced by
computer users using physiological signals including heart rate, acquired using a smart-
watch; respiration, derived from a smartphoneâs acc placed on the chest; and trapezius
electromyography, using proprietary electromyography sensors. Two interactive proto-
cols were implemented to collect data from 12 individuals. Time and frequency domain
features were extracted from the heart rate and electromyography signals, and statistical
and temporal features were extracted from the derived respiration signal.
Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor
were employed for mental stress classification. Different input modalities were tested
for the machine learning models: one for each physiological signal and a multimodal
one, combining all of them. Random Forest obtained the best mean accuracy (98.5%) for
the respiration model whereas K-Nearest-Neighbor attained higher mean accuracies for
the heart rate (89.0%) left, right and total electromyography (98.9%, 99.2%, and 99.3%)
models. KNN algorithm was also able to achieve 100% mean accuracy for the multimodal
model. A possible future approach would be to validate these models in real-time.O stress mental é uma condição amplamente prevalente direta ou indiretamente
responsåvel por quase metade de todas doenças relacionadas com trabalho. O stress expe-
rienciado no trabalho Ă© o segundo problema de saĂșde ocupacional com maior impacto na
Europa, depois das doenças mĂșsculo-esquelĂ©ticas. Quando a saĂșde mental Ă© adequada-
mente cuidada, o bem-estar, o desempenho e a produtividade de um trabalhador podem
ser consideravelmente melhorados.
Esta tese apresenta modelos de aprendizagem automĂĄtica que classificam o stress
mental experienciado por utilizadores de computadores recorrendo a sinais fisiolĂłgi-
cos, incluindo a frequĂȘncia cardĂaca, adquirida pelo sensor de fotopletismografia de um
smartwatch; a respiração, derivada de um acelerómetro incorporado no smartphone po-
sicionado no peito; e electromiografia de cada um dos mĂșsculos trapĂ©zios, utilizando
sensores electromiogrĂĄficos proprietĂĄrios. Foram implementados dois protocolos inte-
ractivos para recolha de dados de 12 indivĂduos. CaracterĂsticas do domĂnio temporal
e de frequĂȘncia foram extraĂdas dos sinais de frequĂȘncia cardĂaca e electromiografia, e
caracterĂsticas estatĂsticas e temporais foram extraĂdas do sinal respiratĂłrio.
TrĂȘs algoritmos entitulados K-Nearest-Neighbor, Random Forest, e Support Vector
Machine foram utilizados para a classificação do stress mental. Foram testadas diferentes
modalidades de dados para os modelos de aprendizagem automĂĄtica: uma para cada sinal
fisiolĂłgico e uma multimodal, combinando os trĂȘs. O Random Forest obteve a melhor
precisão média (98,5%) para o modelo de respiração enquanto que o K-Nearest-Neighbor
atingiu uma maior precisĂŁo mĂ©dia nos modelos de frequĂȘncia cardĂaca (89,0%) e electro-
miografia esquerda, direita e total (98,9%, 99,2%, e 99,3%). O algoritmo KNN conseguiu
ainda atingir uma precisĂŁo mĂ©dia de 100% para o modelo multimodal. Uma possĂvel
abordagem futura seria efetuar uma validação destes modelos em tempo real
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