research-article

Towards a model of nonverbal leadership in unstructured joint physical activity

Authors:

Radoslaw Niewiadomski,

Maurizio Mancini,

Antonio CamurriAuthors Info & Claims

MOCO '18: Proceedings of the 5th International Conference on Movement and Computing

Article No.: 20, Pages 1 - 8

https://doi.org/10.1145/3212721.3212816

Published: 28 June 2018 Publication History

Abstract

In this paper, we propose a set of algorithms to compute the cues of the nonverbal leadership in an unstructured joint full-body physical activity, i.e., the joint activity of two or more interacting persons who perform some movements without a predefined sequence and without a predefined leader. An example of such activity can be a contact dance improvisation.

The paper is composed of three parts: cue set, dataset and algorithms. First, we propose a cue set of nonverbal leadership which is grounded on existing literature and studies. It is composed of eight cues that characterize the nonverbal behaviors of the leader in a joint full-body physical activity.

In this paper we also introduce a new dataset. It consists of multimodal data (video, MoCap) of contact dance improvisations. Additionally, sensory deprivation conditions (vision and/or touch restraint) were introduced to collect the evidences of the various strategies used by leaders and followers during improvisation. The dataset was annotated by twenty-seven persons who carried out continuous annotation of leadership in the recorded material.

In the last part of the paper, we propose a set of algorithms that works on positional 3D data (i.e., joints' positions obtained from motion capture data of dancers). Each algorithm models one among the discussed cues of the nonverbal leadership.

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Cited By

Gulino CNiewiadomski R(2024)Sounding bodies: Exploring sonification to promote physical contactProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656749(1-3)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656749
史俊(2023)Gaze Target Detection Based on Predictive Consistency EmbeddingJournal of Image and Signal Processing10.12677/JISP.2023.12201512:02(144-157)Online publication date: 2023
https://doi.org/10.12677/JISP.2023.122015
Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
Show More Cited By

Index Terms

Towards a model of nonverbal leadership in unstructured joint physical activity
1. Applied computing
  1. Arts and humanities
    1. Performing arts
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models

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MOCO '18: Proceedings of the 5th International Conference on Movement and Computing

June 2018

329 pages

ISBN:9781450365048

DOI:10.1145/3212721

Copyright © 2018 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 the author(s) 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: 28 June 2018

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Horizon 2020 Framework Programme

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MOCO '18

MOCO '18: 5th International Conference on Movement and Computing

June 28 - 30, 2018

Genoa, Italy

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Overall Acceptance Rate 85 of 185 submissions, 46%

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Cited By

Gulino CNiewiadomski R(2024)Sounding bodies: Exploring sonification to promote physical contactProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656749(1-3)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656749
史俊(2023)Gaze Target Detection Based on Predictive Consistency EmbeddingJournal of Image and Signal Processing10.12677/JISP.2023.12201512:02(144-157)Online publication date: 2023
https://doi.org/10.12677/JISP.2023.122015
Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
Tonini FBeyan CRicci E(2022)Multimodal Across Domains Gaze Target DetectionProceedings of the 2022 International Conference on Multimodal Interaction10.1145/3536221.3556624(420-431)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3536221.3556624
Thakur SBEYAN CMorerio PDel Bue A(2021)Predicting Gaze from Egocentric Social Interaction Videos and IMU DataProceedings of the 2021 International Conference on Multimodal Interaction10.1145/3462244.3479954(717-722)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3462244.3479954
Lussu VNiewiadomski RVolpe GCamurri A(2019)The role of respiration audio in multimodal analysis of movement qualitiesJournal on Multimodal User Interfaces10.1007/s12193-019-00302-114:1(1-15)Online publication date: 11-Apr-2019
https://doi.org/10.1007/s12193-019-00302-1

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