Emotion Recognition from Body Expressions with a Neural Network Architecture

Elfaramawy, Nourhan; Barros, Pablo; Parisi, German I.; Wermter, Stefan

doi:10.1145/3125739.3125772

Cited by 24 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An interesting approach was proposed in Elfaramawy et al ( 2017 ), where neural network architecture was designed for classifying 6 emotions from body motion patterns. In particular, the classification was performed by grow when required (GWR) networks, self-organizing architectures able to grow nodes whenever the network does not sufficiently match the input.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

View full text Add to dashboard Cite

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

View full text Add to dashboard Cite

show abstract

“…The neural update rate decreases as the neurons become more habituated, which has the effect of preventing that noisy input interferes with consolidated neural representations. Similar GWR-based approaches have been proposed for the incremental learning of body motion patterns [54,8,61] and human-object interaction [55].…”

Section: Growing Self-organizing Networkmentioning

confidence: 99%

Online Continual Learning on Sequences

Parisi¹,

Lomonaco

2020

Recent Trends in Learning From Data

Self Cite

View full text Add to dashboard Cite

Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples. Learning continually in a single data pass is crucial for agents and robots operating in changing environments and required to acquire, fine-tune, and transfer increasingly complex representations from non-i.i.d. input distributions. Machine learning models that address OCL must alleviate catastrophic forgetting in which hidden representations are disrupted or completely overwritten when learning from streams of novel input. In this chapter, we summarize and discuss recent deep learning models that address OCL on sequential input through the use (and combination) of synaptic regularization, structural plasticity, and experience replay. Different implementations of replay have been proposed that alleviate catastrophic forgetting in connectionists architectures via the re-occurrence of (latent representations of) input sequences and that functionally resemble mechanisms of hippocampal replay in the mammalian brain. Empirical evidence shows that architectures endowed with experience replay typically outperform architectures without in (online) incremental learning tasks.

show abstract

“…There is evidence demonstrating that emotional states (e.g., happiness, sadness, anger) [72][73][74] and internal states such as engagement [75] can be recognised from gesture and posture information collected through standard digital video devices. Similarly, body postures captured using the Microsoft Xbox Kinect device were successfully used to classify emotional states [76].…”

Section: Intention Recognition In Social Roboticsmentioning

confidence: 99%

“…Ramey and colleagues [78] for example, integrated the Kinect device into a social robot for tracking and recognising hand gestures. Similarly, Elfaramawy and colleagues [74] mounted a depth sensor onto a Nao robot to record movement data during an interaction with human users. This data was then used to classify whether the interaction partner was expressing the emotions anger, fear, happiness, sadness or surprise.…”

Section: Intention Recognition In Social Roboticsmentioning

confidence: 99%

Requirements for Robotic Interpretation of Social Signals “in the Wild”: Insights from Diagnostic Criteria of Autism Spectrum Disorder

et al. 2020

View full text Add to dashboard Cite

The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to characterise this, and emphasise that future research should tackle this explicitly in realistic settings.

show abstract

Emotion Recognition from Body Expressions with a Neural Network Architecture

Cited by 24 publications

References 21 publications

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

Online Continual Learning on Sequences

Requirements for Robotic Interpretation of Social Signals “in the Wild”: Insights from Diagnostic Criteria of Autism Spectrum Disorder

Contact Info

Product

Resources

About