Evidence map›Paper›PMID 39703744›Full record

ArticleFrontiers in robotics and AI2024

Music, body, and machine: gesture-based synchronization in human-robot musical interaction.

Xuedan Gao, Amit Rogel, Raghavasimhan Sankaranarayanan, Brody Dowling, Gil Weinberg

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xuedan GaoRobotic Musicianship Lab, Center for Music Technology, Georgia Institute of Technology, Atlanta, GA, United States.
Amit RogelRobotic Musicianship Lab, Center for Music Technology, Georgia Institute of Technology, Atlanta, GA, United States.
Raghavasimhan SankaranarayananRobotic Musicianship Lab, Center for Music Technology, Georgia Institute of Technology, Atlanta, GA, United States.
Brody DowlingRobotic Musicianship Lab, Center for Music Technology, Georgia Institute of Technology, Atlanta, GA, United States.
Gil WeinbergRobotic Musicianship Lab, Center for Music Technology, Georgia Institute of Technology, Atlanta, GA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Musical performance relies on nonverbal cues for conveying information among musicians. Human musicians use bodily gestures to communicate their interpretation and intentions to their collaborators, from mood and expression to anticipatory cues regarding structure and tempo. Robotic Musicians can use their physical bodies in a similar way when interacting with fellow musicians. The paper presents a new theoretical framework to classify musical gestures and a study evaluating the effect of robotic gestures on synchronization between human musicians and Shimon - a robotic marimba player developed at Georgia Tech. Shimon utilizes head and arm movements to signify musical information such as expected notes, tempo, and beat. The study, in which piano players were asked to play along with Shimon, assessed the effectiveness of these gestures on human-robot synchronization. Subjects were evaluated for their ability to synchronize with unknown tempo changes as communicated by Shimon's ancillary and social gestures. The results demonstrate the significant contribution of non-instrumental gestures to human-robot synchronization, highlighting the importance of non-music-making gestures for anticipation and coordination in human-robot musical collaboration. Subjects also indicated more positive feelings when interacting with the robot's ancillary and social gestures, indicating the role of these gestures in supporting engaging and enjoyable musical experiences.

Indexed as

human-robot interactionmusicrobotic gesturesrobotic musicianshiprobotssynchronization

Identifiers

PMID39703744
PMCPMC11655300

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.