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1. Behavioral and Physiological Signals
Based Deep Multimodal Approach for
Mobile Emotion Recognition
Abstract
With the rapid development of mobile and wearable devices, it is increasingly
possible to access users’ affective data in a more unobtrusive manner. On this
basis, researchers have proposed various systems to recognize user’s
emotional states. However, mos
learning techniques and a limited number of signals, leading to systems that
either do not generalize well or would frequently lack sufficient information for
emotion detection in realistic scenarios. In this
attention-based LSTM system that uses a combination of sensors from a
smartphone (front camera, microphone, touch panel) and a wristband
(photoplethysmography, electrodermal activity, and infrared thermopile
sensor) to accurately determine user’s emotional states. We evaluated the
proposed system by conducting a user study with 45 participants. Using
collected behavioral (facial expression, speech, keystroke) and physiological
Behavioral and Physiological Signals
Based Deep Multimodal Approach for
Mobile Emotion Recognition
With the rapid development of mobile and wearable devices, it is increasingly
possible to access users’ affective data in a more unobtrusive manner. On this
basis, researchers have proposed various systems to recognize user’s
emotional states. However, most of these studies rely on traditional machine
learning techniques and a limited number of signals, leading to systems that
either do not generalize well or would frequently lack sufficient information for
emotion detection in realistic scenarios. In this paper, we propose a novel
based LSTM system that uses a combination of sensors from a
smartphone (front camera, microphone, touch panel) and a wristband
(photoplethysmography, electrodermal activity, and infrared thermopile
determine user’s emotional states. We evaluated the
proposed system by conducting a user study with 45 participants. Using
collected behavioral (facial expression, speech, keystroke) and physiological
Behavioral and Physiological Signals-
Based Deep Multimodal Approach for
With the rapid development of mobile and wearable devices, it is increasingly
possible to access users’ affective data in a more unobtrusive manner. On this
basis, researchers have proposed various systems to recognize user’s
t of these studies rely on traditional machine
learning techniques and a limited number of signals, leading to systems that
either do not generalize well or would frequently lack sufficient information for
paper, we propose a novel
based LSTM system that uses a combination of sensors from a
smartphone (front camera, microphone, touch panel) and a wristband
(photoplethysmography, electrodermal activity, and infrared thermopile
determine user’s emotional states. We evaluated the
proposed system by conducting a user study with 45 participants. Using
collected behavioral (facial expression, speech, keystroke) and physiological

2. (blood volume, electrodermal activity, skin temperature) affective responses
induced by visual stimuli, our system was able to achieve an average
accuracy of 89.2 percent for binary positive and negative emotion
classification under leave-one-participant-out cross-validation. Furthermore,
we investigated the effectiveness of different combinations of data signals to
cover different scenarios of signal availability.