Social agent: expressions driven by an electronic nose

Loutfi, Amy; Widmark, Jörgen; Wikström, Ewa; Wide, Peter

doi:10.1109/vecims.2003.1227037

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Our work is the first approach that combines information from the Fig. 3: Affect Space and Discrete Emotions: All discrete emotions can be represented by points on a 2D affect space of Valence and Arousal from the PAD model [28], [11].…”

Section: Emotion Characteristics From Trajectorymentioning

confidence: 99%

The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments

Bera,

Randhavane,

Prinja

et al. 2019

Preprint

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We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesianinference, CNN-based learning, and the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low-to medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot.

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Section: Emotion Characteristics From Trajectorymentioning

confidence: 99%

The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments

Bera,

Randhavane,

Prinja

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Fig. 2: All discrete emotions can be represented by points on a 2D affect space of Valence and Arousal [13], [14].…”

mentioning

confidence: 99%

Identifying Emotions from Walking using Affective and Deep Features

Randhavane,

Bhattacharya,

Kapsaskis

et al. 2019

Preprint

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We present a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking, we extract his/her walking gait in the form of a series of 3D poses. Our goal is to exploit the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. Our perceived emotion recognition approach uses deep features learned via LSTM on labeled emotion datasets. Furthermore, we combine these features with affective features computed from gaits using posture and movement cues. These features are classified using a Random Forest Classifier. We show that our mapping between the combined feature space and the perceived emotional state provides 80.07% accuracy in identifying the perceived emotions. In addition to classifying discrete categories of emotions, our algorithm also predicts the values of perceived valence and arousal from gaits. We also present an "EWalk (Emotion Walk)" dataset that consists of videos of walking individuals with gaits and labeled emotions. To the best of our knowledge, this is the first gait-based model to identify perceived emotions from videos of walking individuals.

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