Interpretation of Social Touch on an Artificial Arm Covered with an EIT-based Sensitive Skin

Silvera-Tawil, David; Rye, David; Velonaki, Mari

doi:10.1007/s12369-013-0223-x

Cited by 64 publications

(71 citation statements)

References 55 publications

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“…Increasing the number of touch gesture by adding 'pull', 'slap' and 'squeeze', they compared the classification accuracy obtained with the LogitBoost algorithm (71%) with human recognition (90%). Further experiments were shown in [38] by interpreting social touch on same artificial skin. Up to 90% accuracy were achieved using Logitboost algorithm for both emotions and social messages, similar with human recognition.…”

Section: Related Workmentioning

confidence: 99%

Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets

Gaus

Olugbade

Jan

et al. 2015

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

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Touch is a primary nonverbal communication channel used to communicate emotions or other social messages. A variety of social touch exists including hugging, rubbing and punching. Despite its importance, this channel is still very little explored in the affective computing field, as much more focus has been placed on visual and aural channels. In this paper, we investigate the possibility to automatically discriminate between different social touch types. We propose five distinct feature sets for describing touch behaviours captured by a grid of pressure sensors. These features are then combined together by using the Random Forest and Boosting methods for categorizing the touch gesture type. The proposed methods were evaluated on both the HAART (7 gesture types over different surfaces) and the CoST (14 gesture types over the same surface) datasets made available by the Social Touch Gesture Challenge 2015. Well above chance level performances were achieved with a 67% accuracy for the HAART and 59% for the CoST testing datasets respectively.

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Section: Related Workmentioning

confidence: 99%

Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets

Gaus

Olugbade

Jan

et al. 2015

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

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“…A shift towards socially interactive robots can be seen in their increased application in a variety of roles for use as social and behavioural facilitators, for example, in the area of elderly care (e.g., [1]) and for use in human interaction-therapy (e.g., [2,3]). As interaction between humans and robots is becoming more variegated, an increasing interest is emerging in designing robots with human-like features and qualities that enable interaction with humans in more intuitive and meaningful ways [4,5]. Touch as one of the most fundamental aspects of human social interaction [6] has started to receive interest in human-robot interaction (HRI) research (for an overview see e.g., [7,8]) and it has been argued that enabling robots to "feel", "understand", and respond to touch in accordance with expectations of the human would enable a more intuitive interaction between humans and robots [8].…”

Section: Introductionmentioning

confidence: 99%

Designing for a Wearable Affective Interface for the NAO Robot: A Study of Emotion Conveyance by Touch

Lowe

Andreasson

Alenljung

et al. 2018

MTI

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Abstract:We here present results and analysis from a study of affective tactile communication between human and humanoid robot (the NAO robot). In the present work, participants conveyed eight emotions to the NAO via touch. In this study, we sought to understand the potential for using a wearable affective (tactile) interface, or WAffI. The aims of our study were to address the following: (i) how emotions and affective states can be conveyed (encoded) to such a humanoid robot, (ii) what are the effects of dressing the NAO in the WAffI on emotion conveyance and (iii) what is the potential for decoding emotion and affective states. We found that subjects conveyed touch for longer duration and over more locations on the robot when the NAO was dressed with WAffI than when it was not. Our analysis illuminates ways by which affective valence, and separate emotions, might be decoded by a humanoid robot according to the different features of touch: intensity, duration, location, type. Finally, we discuss the types of sensors and their distribution as they may be embedded within the WAffI and that would likely benefit Human-NAO (and Human-Humanoid) interaction along the affective tactile dimension.

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“…[26]), sensor sheets (e.g. [30]) or human body parts such as arms [32]. Physical appearances of interfaces for touch interaction included robotic animals (e.g.…”

Section: Touch Surface and Sensorsmentioning

confidence: 99%

“…[26]), partial embodiments such as a mannequin arm (e.g. [33]) and a balloon interface [29]. Several techniques were used for the sensing of touch, each having its own advantages and drawbacks for example, low cost vs. large hysteresis in force sensing resistors [8].…”

Section: Touch Surface and Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

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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Interpretation of Social Touch on an Artificial Arm Covered with an EIT-based Sensitive Skin

Cited by 64 publications

References 55 publications

Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets

Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets

Designing for a Wearable Affective Interface for the NAO Robot: A Study of Emotion Conveyance by Touch

Automatic recognition of touch gestures in the corpus of social touch

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