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A Movement in Multiple Time Neural Network for Automatic Detection of Pain Behaviour

Authors:

Temitayo Olugbade,

Amanda C de C Williams,

Nadia Bianchi-BerthouzeAuthors Info & Claims

ICMI '20 Companion: Companion Publication of the 2020 International Conference on Multimodal Interaction

Pages 442 - 445

https://doi.org/10.1145/3395035.3425969

Published: 27 December 2020 Publication History

ICMI '20 Companion: Companion Publication of the 2020 International Conference on Multimodal Interaction

A Movement in Multiple Time Neural Network for Automatic Detection of Pain Behaviour

Pages 442 - 445

Abstract
References

Abstract

The use of multiple clocks has been a favoured approach to modelling the multiple timescales of sequential data. Previous work based on clocks and multi-timescale studies in general have not clearly accounted for multidimensionality of data such that each dimension has its own timescale(s). Focusing on body movement data which has independent yet coordinating degrees of freedom, we propose a Movement in Multiple Time (MiMT) neural network. Our MiMT models multiple timescales by learning different levels of movement interpretation (i.e. labels) and further allows for separate timescales across movements dimensions. We obtain 0.75 and 0.58 average F1 scores respectively for binary frame-level and three-class window-level classification of pain behaviour based on the MiMT. Findings in ablation studies suggest that these two elements of the MiMT are valuable to modelling multiple timescales of multidimensional sequential data.

References

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Chongyang Wang, Temitayo Olugbade, Akhil Mathur, Amanda C De C. Williams, Nicholas Lane, and Nadia Bianchi-Berthouze. 2019 a. Recurrent network based automatic detection of chronic pain protective behavior using mocap and semg data. In Proceedings of International Symposium on Wearable Computers. 225--230.

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Chongyang Wang, Min Peng, Temitayo Olugbade, Nicholas Lane, Amanda C de C Williams, and Nadia Bianchi-Berthouze. 2019 b. Learning temporal and bodily attention in protective movement behavior detection. In Proceedings of Affective Computing and Intelligent Interaction Workshops and Demos. 324--330.

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Cited By

Olugbade TWilliams AGold NBianchi-Berthouze N(2024)Movement Representation Learning for Pain Level ClassificationIEEE Transactions on Affective Computing10.1109/TAFFC.2023.333452215:3(1303-1314)Online publication date: Jul-2024
https://doi.org/10.1109/TAFFC.2023.3334522
Dehshibi MOlugbade TDiaz-de-Maria FBianchi-Berthouze NTajadura-Jiménez A(2023)Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNsIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2023.326235817:3(677-688)Online publication date: May-2023
https://doi.org/10.1109/JSTSP.2023.3262358

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A Movement in Multiple Time Neural Network for Automatic Detection of Pain Behaviour

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Information

Published In

cover image ACM Conferences

ICMI '20 Companion: Companion Publication of the 2020 International Conference on Multimodal Interaction

October 2020

548 pages

ISBN:9781450380027

DOI:10.1145/3395035

General Chairs:
Khiet Truong
University of Twente, the Netherlands
,
Dirk Heylen
University of Twente, the Netherlands
,
Mary Czerwinski
Microsoft Research, USA
,
Program Chairs:
Nadia Berthouze
University College London, United Kingdom
,
Mohamed Chetouani
Sorbonne University, France
,
Mikio Nakano
C4A Research Institute, Japan

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2020

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Horizon 2020

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ICMI '20

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SIGCHI

ICMI '20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 25 - 29, 2020

Virtual Event, Netherlands

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Cited By

Olugbade TWilliams AGold NBianchi-Berthouze N(2024)Movement Representation Learning for Pain Level ClassificationIEEE Transactions on Affective Computing10.1109/TAFFC.2023.333452215:3(1303-1314)Online publication date: Jul-2024
https://doi.org/10.1109/TAFFC.2023.3334522
Dehshibi MOlugbade TDiaz-de-Maria FBianchi-Berthouze NTajadura-Jiménez A(2023)Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNsIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2023.326235817:3(677-688)Online publication date: May-2023
https://doi.org/10.1109/JSTSP.2023.3262358

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