The Grenoble System for the Social Touch Challenge at ICMI 2015

Ta, Viet-Cuong; Johal, Wafa; Portaz, Maxime; Castelli, Eric; Vaufreydaz, Dominique

doi:10.1145/2818346.2830598

Cited by 20 publications

(38 citation statements)

References 20 publications

(42 reference statements)

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“…This indicated that the additional features and the use of more complex classification methods with hyperparameter optimization have improved the accuracy. Results reported in this paper fall within the range of 26-61 % accuracy that was reported for a data challenge using the CoST data set [3,12,18,36]. Our results are comparable to those reported in the work of Gaus et al and Ta et al who reported accuracies up to 59 and 61 %, respectively using random forest [12,36].…”

Section: Classification Results and Touch Gesture Confusionsupporting

confidence: 90%

“…Results reported in this paper fall within the range of 26-61 % accuracy that was reported for a data challenge using the CoST data set [3,12,18,36]. Our results are comparable to those reported in the work of Gaus et al and Ta et al who reported accuracies up to 59 and 61 %, respectively using random forest [12,36]. However it should be noted that the data challenge contained a subset of CoST (i.e., gentle and normal variants) and that the train and test data division was different from the leave-one-subjectout cross-validation results reported in this paper [24].…”

Section: Classification Results and Touch Gesture Confusionsupporting

confidence: 75%

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Automatic recognition of touch gestures in the corpus of social touch

Jung

Poel

Poppe

et al. 2016

J Multimodal User Interfaces

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For an artifact such as a robot or a virtual agent to respond appropriately to human social touch behavior, it should be able to automatically detect and recognize touch. This paper describes the data collection of CoST: Corpus of Social Touch, a data set containing 7805 captures of 14 different social touch gestures. All touch gestures were performed in three variants: gentle, normal and rough on a pressure sensor grid wrapped around a mannequin arm. Recognition of these 14 gesture classes using various classifiers yielded accuracies up to 60 %; moreover, gentle gestures proved to be harder to classify than normal and rough gestures. We further investigated how different classifiers, interpersonal differences, gesture confusions and gesture variants affected the recognition accuracy. Finally, we present directions for further research to ensure proper transfer of the touch modality from interpersonal interaction to areas such as human-robot interaction (HRI).

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Section: Classification Results and Touch Gesture Confusionsupporting

confidence: 90%

Section: Classification Results and Touch Gesture Confusionsupporting

confidence: 75%

Automatic recognition of touch gestures in the corpus of social touch

Jung

Poel

Poppe

et al. 2016

J Multimodal User Interfaces

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“…Ta et al [108] random forest a 61.3% Ta et al [108] random forest b 60.8% Ta et al [108] SVM b 60.5% Ta et al [108] SVM a 59.9% Gaus et al [41] random forest 58.7% Gaus et al [41] multiboost 58.2% Hughes et al [53] logistic regression 47.2%…”

Section: Papermentioning

confidence: 99%

“…This increased the difficulty of automatic segmentation based on pressure differences over time. Ta et al explored additional techniques for automatic segmentation to further reduce the number of excess frames [108]. However, these methods for automatic segmentation did not improve classification.…”

Section: Data Pre-processingmentioning

confidence: 99%

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Socially intelligent robots that understand and respond to human touch

Jung¹

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Touch is an important nonverbal form of interpersonal interaction which is used to communicate emotions and other social messages. As interactions with social robots are likely to become more common in the near future these robots should also be able to engage in tactile interaction with humans. Therefore, the aim of the research presented in this dissertation is to work towards socially intelligent robots that can understand and respond to human touch. To become a socially intelligent actor a robot must be able to sense, classify and interpret human touch and respond to this in an appropriate manner. To this end we present work that addresses different parts of this interaction cycle.After the introduction in Part I of the dissertation, we have taken a datadriven approach in Part II. We have focused on the sense and classify steps of the interaction cycle to automatically recognize social touch gestures such as pat, stroke and tickle from pressure sensor data.In Chapter 2 we present CoST: Corpus of Social Touch, a dataset containing 7805 captures of 14 different social touch gestures. All touch gestures were performed in three variants: gentle, normal and rough on a pressure sensitive mannequin arm. Recognition of these 14 gesture classes using various classifiers yielded accuracies of up to 60%; moreover, gentle gestures proved to be harder to classify than normal and rough gestures. We further investigated how different classifiers, interpersonal differences, gesture confusions and gesture variants affected the recognition accuracy.In Chapter 3 we describe the outcome of a machine learning challenge on touch gesture recognition. This challenge was extended to the research community working on multimodal interaction with the goal of sparking interest in the touch modality and to promote exploration of the use of data processing techniques from other more mature modalities for touch recognition. Two datasets were made available containing labeled pressure sensor data of social touch gestures: the CoST dataset presented in Chapter 2 and the Human-Animal Affective Robot Touch (HAART) gesture set. The most important outcomes of the challenges were: (1) transferring techniques from other modalities, such as image processing, speech, and human action recognition provided valuable feature sets; (2) gesture classification confusions were similar despite the various data processing methods that were used. vii In Part III of the dissertation we present three studies on the use of social touch in interaction with robot pets. We have mainly focused on the interpret and respond steps of the interaction cycle to identify which touch gestures a robot pet should understand, how touch can be interpreted within a social context and in which ways a robot can respond to human touch.In Chapter 4 we present a study of which the aim was to gain more insight into the factors that are relevant to interpret the meaning of touch within a social context. We elicited touch behaviors by letting participants interact with a robot pet com...

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An Ensemble Classifier Based on Three-Way Decisions for Social Touch Gesture Recognition

Zhang

Liu

Shi

et al. 2018

Lecture Notes in Computer Science

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Abstract. Social touch is an important form of social interaction. In Human Robot Interaction (HRI), touch can provide additional information to other modalities, such as audio, visual. One of the application is the robot therapy that has great social significance. In this paper, an ensemble classifier based on threeway decisions is proposed to recognize touch gestures. Firstly, features are extracted from on six perspectives and four classifiers are constructed on different scales with different pre-processing methods. . Then an ensemble classifier is used to combine the four classifiers to classify the gestures.The proposed method is tested on the public Corpus of Social Touch (Cost) dataset. The experiments results not only verify the validity of our method but also show the better accuracy of our ensemble classifier.

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The Grenoble System for the Social Touch Challenge at ICMI 2015

Cited by 20 publications

References 20 publications

Automatic recognition of touch gestures in the corpus of social touch

Automatic recognition of touch gestures in the corpus of social touch

Socially intelligent robots that understand and respond to human touch

An Ensemble Classifier Based on Three-Way Decisions for Social Touch Gesture Recognition

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