Towards the quantification of human-robot imitation using wearable inertial sensors

Miguel P. Xochicale; Chris Baber; Mourad Oussalah

doi:10.1145/3029798.3038320

Towards the quantification of human-robot imitation using wearable inertial sensors

Miguel P. Xochicale, Chris Baber, Mourad Oussalah

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this study, we propose a metric in order to quantify how closely a healthy participant imitates a robot, for which we use inertial sensors attached to both individual participant and to a humanoid-robot. For the experiment, twelve healthy participants were invited to perform simple arm movements in order to apply the state space reconstruction which is based on the method of time-delay embedding and PCA. Although the performed arm movements of the healthy participants were very simple, the study reveals that the participants showed different ranges of the proposed metric that can be linked to the level of imitation. Such a metric can be improved in order to determine a detailed scoring of human-robot imitation during training or rehabilitation activities.

Original language	English
Title of host publication	HRI 2017 - Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
Publisher	IEEE Computer Society
Pages	327-328
Number of pages	2
ISBN (Electronic)	9781450348850
DOIs	https://doi.org/10.1145/3029798.3038320
Publication status	Published - 6 Mar 2017
Event	12th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017 - Vienna, Austria Duration: 6 Mar 2017 → 9 Mar 2017

Publication series

Name	ACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)	2167-2148

Conference

Conference	12th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017
Country/Territory	Austria
City	Vienna
Period	6/03/17 → 9/03/17

Keywords

human-robot imitation
movement variability
non-linear dynamics
state space reconstruction
wearable inertial sensors

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3029798.3038320

Cite this

Xochicale, M. P., Baber, C., & Oussalah, M. (2017). Towards the quantification of human-robot imitation using wearable inertial sensors. In HRI 2017 - Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 327-328). (ACM/IEEE International Conference on Human-Robot Interaction). IEEE Computer Society. https://doi.org/10.1145/3029798.3038320

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abstract = "In this study, we propose a metric in order to quantify how closely a healthy participant imitates a robot, for which we use inertial sensors attached to both individual participant and to a humanoid-robot. For the experiment, twelve healthy participants were invited to perform simple arm movements in order to apply the state space reconstruction which is based on the method of time-delay embedding and PCA. Although the performed arm movements of the healthy participants were very simple, the study reveals that the participants showed different ranges of the proposed metric that can be linked to the level of imitation. Such a metric can be improved in order to determine a detailed scoring of human-robot imitation during training or rehabilitation activities.",

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author = "Xochicale, {Miguel P.} and Chris Baber and Mourad Oussalah",

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Xochicale, MP, Baber, C & Oussalah, M 2017, Towards the quantification of human-robot imitation using wearable inertial sensors. in HRI 2017 - Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM/IEEE International Conference on Human-Robot Interaction, IEEE Computer Society, pp. 327-328, 12th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017, Vienna, Austria, 6/03/17. https://doi.org/10.1145/3029798.3038320

Towards the quantification of human-robot imitation using wearable inertial sensors. / Xochicale, Miguel P.; Baber, Chris; Oussalah, Mourad.
HRI 2017 - Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. IEEE Computer Society, 2017. p. 327-328 (ACM/IEEE International Conference on Human-Robot Interaction).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this study, we propose a metric in order to quantify how closely a healthy participant imitates a robot, for which we use inertial sensors attached to both individual participant and to a humanoid-robot. For the experiment, twelve healthy participants were invited to perform simple arm movements in order to apply the state space reconstruction which is based on the method of time-delay embedding and PCA. Although the performed arm movements of the healthy participants were very simple, the study reveals that the participants showed different ranges of the proposed metric that can be linked to the level of imitation. Such a metric can be improved in order to determine a detailed scoring of human-robot imitation during training or rehabilitation activities.

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Towards the quantification of human-robot imitation using wearable inertial sensors

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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