Touch Recognition with Attentive End-to-End Model
Authors: 
Wail El Bani and Mohamed ChetouaniAuthors Info & Claims
Wail El Bani and Mohamed ChetouaniAuthors Info & ClaimsOctober 2020
Pages 694 - 698
Abstract
Touch is the earliest sense to develop and the first mean of contact with the external world. Touch also plays a key role in our socio-emotional communication: we use it to communicate our feelings, elicit strong emotions in others and modulate behavior (e.g compliance). Although its relevance, touch is an understudied modality in Human-Machine-Interaction compared to audition and vision. Most of the social touch recognition systems require a feature engineering step making them difficult to compare and to generalize to other databases. In this paper, we propose an end-to-end approach. We present an attention-based end-to-end model for touch gesture recognition evaluated on two public datasets (CoST and HAART) in the context of the ICMI 15 Social Touch Challenge. Our model gave a similar level of accuracy: 61% for CoST and 68% for HAART and uses self-attention as an alternative to feature engineering and Recurrent Neural Networks.
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Information & Contributors
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Published In
October 2020
920 pages
ISBN:9781450375818
DOI:10.1145/3382507
- General Chairs:
- Khiet Truong,
- Dirk Heylen,
- Mary Czerwinski,
- Program Chairs:
- Nadia Berthouze,
- Mohamed Chetouani,
- Mikio Nakano
Copyright © 2020 ACM.
© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Published: 22 October 2020
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October 25 - 29, 2020
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