A study on emotion recognition from body gestures using Kinect sensor

Saha, Sriparna; Datta, Shreyasi; Konar, Amit; Janarthanan, R.

doi:10.1109/iccsp.2014.6949798

Cited by 72 publications

(47 citation statements)

References 12 publications

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“…We have validated our proposed work's performance with back propagation neural network (BPNN) [11], recurrent neural network (RNN) [6], feed forward neural network (FFNN) [12], ensemble classifier using binary tree (ECBT) [13], linear support vector machine (LSVM) [14], support vector machine with radial basis function (RBFSVM) [2], knearest neighbor (kNN). Neural networks are biologically inspired classification algorithms.…”

Section: Resultsmentioning

confidence: 99%

Human skeleton matching for e-learning of dance using a probabilistic neural network

Saha

Lahiri

Konar

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-With the growing interest in the domain of human computer interaction (HCI) these days, budding research professionals are coming up with novel ideas of developing more versatile and flexible modes of communication between a man and a machine. Using the attributes of internet, the scientists have been able to create a web based social platform for learning any desired art by the subject himself/herself, and this particular procedure is termed as electronic learning or e-learning. In this paper, we propose a novel application of gesture dependent elearning of dance. This e-learning procedure may provide help to many dance enthusiasts who cannot learn the art because of the scarcity of resources despite having great zeal. The paper mainly deals with recognition of different dance gestures of a trained user such that after detecting the discrepancies between the gestures shown and actually performed by a novice; the user can rectify his faults. The elementary knowledge of geometry has been employed to introduce the concept of planes in the feature extraction stage. Actually, five planes have been constructed to signify major body parts while keeping the synchronous parts in one unit. Then four distances and four angular features have been obtained to provide entire positional information of the different body joints. Finally, using a probabilistic neural network the dance gestures have been classified after training the said network with sufficient amount of data recorded from numerous subjects to maintain generality.

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Section: Resultsmentioning

confidence: 99%

Human skeleton matching for e-learning of dance using a probabilistic neural network

Saha

Lahiri

Konar

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

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“…Saha et al [122] identified gestures corresponding to five basic human emotional states, namely, anger, fear, happiness, sadness and relaxation from skeletal geometrical features. They compared binary decision tree (BDT), ensemble tree (ET), k-nearest neighbour (KNN) and SVM, obtaining the best results by using ET.…”

Section: Emotion Recognitionmentioning

confidence: 99%

Survey on Emotional Body Gesture Recognition

Noroozi

Corneanu

Kamińska

et al. 2021

IEEE Trans. Affective Comput.

293

130

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Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as "body language" and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.

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“…The main advantage of this measure is its unconscious and objective nature. Three main signals have been used for emotion detection: the facial expressions [11], the body gestures [13], and some biological reactions from the autonomic nervous system, such as skin conductivity, heart rate, skin temperature [3] or pupillary response and eye blinking. Such methods have been experimented for various entertainment applications: music [5], movies and video [14], [4], [17], or advertisement [10].…”

Section: Introductionmentioning

confidence: 99%

Visualizing the Emotional Journey of a Museum

Du¹,

Shu²,

Tong³

et al. 2016

Linköping Electronic Conference Proceedings

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Wearable devices and new types of sensors make it possible to capture people behavior, activity and, potentially, cognitive state in their daily life. Today those devices are mainly used for well-being applications, by recording and displaying people's activity. Some work have been published going a step further by inferring from the recorded signals the emotional state of individuals or group of people. However, the information provided and the way it is presented are still in their infancy, with time lined graphs showing calories, heart-rate, steps, temperature, and sometimes affective intensity. In this paper we present an experiment done during the visit of different people in a museum of arts to capture the emotional impact of the exposed paintings. We also propose an associated visualization of their emotional journey. The emotion is here measured as the affective response to the paintings observation, and the processing algorithm is based on an existing technique adapted to the particular case of different observation durations. The visualization is based on a 3D map of the museum with different colors associated to the different paintings to get the emotional heat-map of the museum (more precisely the arousal dimension). The validation has been done in the museum of arts at Lyon, France, with 46 visitors, for a total of 27 paintings, exposed in three different rooms.

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A study on emotion recognition from body gestures using Kinect sensor

Cited by 72 publications

References 12 publications

Human skeleton matching for e-learning of dance using a probabilistic neural network

Human skeleton matching for e-learning of dance using a probabilistic neural network

Survey on Emotional Body Gesture Recognition

Visualizing the Emotional Journey of a Museum

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