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A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG

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Abstract

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with “one against one” multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future.

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Funding

The authors acknowledge INSPIRE Fellowship, Department of Science & Technology, Ministry of Science & Technology, Govt. of India and Sponsored Research & Industrial Consultancy (SRIC), Indian Institute of Technology Kharagpur, No: IIT/SRIC/ME-SMST/RNC/2014-15/15, dated: 21-01-2014, for providing the financial support. The authors state that they have no financial conflicts while preparing this document.

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Authors and Affiliations

  1. Advanced Technology and Development Centre, Indian Institute of Technology, Kharagpur, India

    Rinku Roy

  2. School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, India

    Debdeep Sikdar & Manjunatha Mahadevappa

  3. Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India

    C. S. Kumar

Authors
  1. Rinku Roy
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  2. Debdeep Sikdar
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  3. Manjunatha Mahadevappa
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  4. C. S. Kumar
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Corresponding author

Correspondence to Manjunatha Mahadevappa.

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This research adhered to the tenets of the Declaration of Helsinki. Written consents were taken from the subjects before participating in the experiment.

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The authors declare that they have no conflict of interest.

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Roy, R., Sikdar, D., Mahadevappa, M. et al. A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG. Med Biol Eng Comput 56, 2095–2107 (2018). https://doi.org/10.1007/s11517-018-1833-0

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  • DOI: https://doi.org/10.1007/s11517-018-1833-0

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