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 proceedingConference 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 languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
PublisherIEEE
Pages2939-2944
ISBN (Electronic)978-1-5386-1645-1
ISBN (Print)978-1-5386-1646-8
DOIs
Publication statusPublished - 1 Dec 2017
Event2017 IEEE Conference on Systems, Man, and Cybernetics - Banff, Alberta, Canada
Duration: 5 Oct 20178 Oct 2017

Conference

Conference2017 IEEE Conference on Systems, Man, and Cybernetics
Abbreviated titleSMC 2017
Country/TerritoryCanada
CityBanff, Alberta
Period5/10/178/10/17

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