A force-driven granular model for emg based grasp recognition

Yinfeng Fang; Dalin Zhou; Kairu Li; Zhaojie Ju; Honghai Liu

doi:10.1109/SMC.2017.8123074

A force-driven granular model for emg based grasp recognition

Yinfeng Fang, Dalin Zhou, Kairu Li, Zhaojie Ju, Honghai Liu

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

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Abstract

It is a challenge to precisely predict hand grasps based on EMG signals given practical scenarios, due to its inherent nature. This paper proposes a solution to tackle the challenge with a force-driven granular model (FDGM). The problem of n-class hand grasp classification has been represented as force-based granular modelling, in which a number of granules are constructed for each class relying on the synchronically captured grasping force. A rule based mechanism is formed for granule generation of each class, and a cross-testing algorithm is proposed to optimise the number of granules. The experiment based on 8-case grasp recognition reveals that the proposed method performs better in terms of motion recognition accuracy of multiple EMG channel combination, and is more insensitive to signal interferences. In comparison with other rules of information granulation, it is confirmed that the force-driven rule is of the most efficiency with comparable classification accuracy. The research outcomes pave the way for real-time prediction of grasps and corresponding force in human-centred environments.

Original language	English
Title of host publication	2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2939-2944
ISBN (Electronic)	978-1-5386-1645-1
ISBN (Print)	978-1-5386-1646-8
DOIs	https://doi.org/10.1109/SMC.2017.8123074
Publication status	Published - 1 Dec 2017
Event	2017 IEEE Conference on Systems, Man, and Cybernetics - Banff, Alberta, Canada Duration: 5 Oct 2017 → 8 Oct 2017

Conference

Conference	2017 IEEE Conference on Systems, Man, and Cybernetics
Abbreviated title	SMC 2017
Country/Territory	Canada
City	Banff, Alberta
Period	5/10/17 → 8/10/17

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10.1109/SMC.2017.8123074

A Force-Driven Granular Model for EMG based Grasp Recognition
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Accepted author manuscript (Post-print), 1.52 MB

https://ieeexplore.ieee.org/document/8123074/

Cite this

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title = "A force-driven granular model for emg based grasp recognition",

abstract = "It is a challenge to precisely predict hand grasps based on EMG signals given practical scenarios, due to its inherent nature. This paper proposes a solution to tackle the challenge with a force-driven granular model (FDGM). The problem of n-class hand grasp classification has been represented as force-based granular modelling, in which a number of granules are constructed for each class relying on the synchronically captured grasping force. A rule based mechanism is formed for granule generation of each class, and a cross-testing algorithm is proposed to optimise the number of granules. The experiment based on 8-case grasp recognition reveals that the proposed method performs better in terms of motion recognition accuracy of multiple EMG channel combination, and is more insensitive to signal interferences. In comparison with other rules of information granulation, it is confirmed that the force-driven rule is of the most efficiency with comparable classification accuracy. The research outcomes pave the way for real-time prediction of grasps and corresponding force in human-centred environments. ",

author = "Yinfeng Fang and Dalin Zhou and Kairu Li and Zhaojie Ju and Honghai Liu",

year = "2017",

month = dec,

day = "1",

doi = "10.1109/SMC.2017.8123074",

language = "English",

isbn = "978-1-5386-1646-8",

pages = "2939--2944",

booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 IEEE Conference on Systems, Man, and Cybernetics, SMC 2017 ; Conference date: 05-10-2017 Through 08-10-2017",

}

Fang, Y, Zhou, D, Li, K, Ju, Z & Liu, H 2017, A force-driven granular model for emg based grasp recognition. in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Institute of Electrical and Electronics Engineers Inc., pp. 2939-2944, 2017 IEEE Conference on Systems, Man, and Cybernetics, Banff, Alberta, Canada, 5/10/17. https://doi.org/10.1109/SMC.2017.8123074

TY - GEN

T1 - A force-driven granular model for emg based grasp recognition

AU - Fang, Yinfeng

AU - Zhou, Dalin

AU - Li, Kairu

AU - Ju, Zhaojie

AU - Liu, Honghai

PY - 2017/12/1

Y1 - 2017/12/1

N2 - It is a challenge to precisely predict hand grasps based on EMG signals given practical scenarios, due to its inherent nature. This paper proposes a solution to tackle the challenge with a force-driven granular model (FDGM). The problem of n-class hand grasp classification has been represented as force-based granular modelling, in which a number of granules are constructed for each class relying on the synchronically captured grasping force. A rule based mechanism is formed for granule generation of each class, and a cross-testing algorithm is proposed to optimise the number of granules. The experiment based on 8-case grasp recognition reveals that the proposed method performs better in terms of motion recognition accuracy of multiple EMG channel combination, and is more insensitive to signal interferences. In comparison with other rules of information granulation, it is confirmed that the force-driven rule is of the most efficiency with comparable classification accuracy. The research outcomes pave the way for real-time prediction of grasps and corresponding force in human-centred environments.

AB - It is a challenge to precisely predict hand grasps based on EMG signals given practical scenarios, due to its inherent nature. This paper proposes a solution to tackle the challenge with a force-driven granular model (FDGM). The problem of n-class hand grasp classification has been represented as force-based granular modelling, in which a number of granules are constructed for each class relying on the synchronically captured grasping force. A rule based mechanism is formed for granule generation of each class, and a cross-testing algorithm is proposed to optimise the number of granules. The experiment based on 8-case grasp recognition reveals that the proposed method performs better in terms of motion recognition accuracy of multiple EMG channel combination, and is more insensitive to signal interferences. In comparison with other rules of information granulation, it is confirmed that the force-driven rule is of the most efficiency with comparable classification accuracy. The research outcomes pave the way for real-time prediction of grasps and corresponding force in human-centred environments.

U2 - 10.1109/SMC.2017.8123074

DO - 10.1109/SMC.2017.8123074

M3 - Conference contribution

SN - 978-1-5386-1646-8

SP - 2939

EP - 2944

BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Conference on Systems, Man, and Cybernetics

Y2 - 5 October 2017 through 8 October 2017

ER -

A force-driven granular model for emg based grasp recognition

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