22,660 research outputs found
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
Exploring Cognitive States: Methods for Detecting Physiological Temporal Fingerprints
Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a physiological temporal fingerprint, that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states
Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation
High frequency psychophysiological data create a challenge for quantitative
modeling based on Big Data tools since they reflect the complexity of processes
taking place in human body and its responses to external events. Here we
present studies of fluctuations in facial electromyography (fEMG) and
electrodermal activity (EDA) massive time series and changes of such signals in
the course of emotional stimulation. Zygomaticus major (ZYG, "smiling" muscle)
activity, corrugator supercilii (COR, "frowning"bmuscle) activity, and phasic
skin conductance (PHSC, sweating) levels of 65 participants were recorded
during experiments that involved exposure to emotional stimuli (i.e., IAPS
images, reading and writing messages on an artificial online discussion board).
Temporal Taylor's fluctuations scaling were found when signals for various
participants and during various types of emotional events were compared. Values
of scaling exponents were close to 1, suggesting an external origin of system
dynamics and/or strong interactions between system's basic elements (e.g.,
muscle fibres). Our statistical analysis shows that the scaling exponents
enable identification of high valence and arousal levels in ZYG and COR
signals
Social Networks through the Prism of Cognition
Human relations are driven by social events - people interact, exchange
information, share knowledge and emotions, or gather news from mass media.
These events leave traces in human memory. The initial strength of a trace
depends on cognitive factors such as emotions or attention span. Each trace
continuously weakens over time unless another related event activity
strengthens it. Here, we introduce a novel Cognition-driven Social Network
(CogSNet) model that accounts for cognitive aspects of social perception and
explicitly represents human memory dynamics. For validation, we apply our model
to NetSense data on social interactions among university students. The results
show that CogSNet significantly improves quality of modeling of human
interactions in social networks
Detecting User Engagement in Everyday Conversations
This paper presents a novel application of speech emotion recognition:
estimation of the level of conversational engagement between users of a voice
communication system. We begin by using machine learning techniques, such as
the support vector machine (SVM), to classify users' emotions as expressed in
individual utterances. However, this alone fails to model the temporal and
interactive aspects of conversational engagement. We therefore propose the use
of a multilevel structure based on coupled hidden Markov models (HMM) to
estimate engagement levels in continuous natural speech. The first level is
comprised of SVM-based classifiers that recognize emotional states, which could
be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM
then uses these emotional states as input, estimating users' engagement in
conversation by decoding the internal states of the HMM. We report experimental
results obtained by applying our algorithms to the LDC Emotional Prosody and
CallFriend speech corpora.Comment: 4 pages (A4), 1 figure (EPS
A dynamic texture based approach to recognition of facial actions and their temporal models
In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set
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