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

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. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.

Fingertip interactive tracking registration method for AR assembly system

“…It can be seen from Table 3 that the average precision of fingertip detection and recognition algorithm based on K-COS and parallel vector is about 93%, and the average processing time of fingertip detection algorithm in this paper is lower than that in literature (Guo and Leng 2019), literature (Pei-Hsuan et al 2018) and literature (Liu and Song 2020), while the complexity of the fingertip detection algorithm in literature (Roy et al 2018) is slightly lower than that in this paper because of the circular feature of fingertips to remove noise fingertip strategy. However, the small increase of fingertip detection time does not affect the use of mobile AR assembly system.…”

An Electro-Oculogram Based Vision System for Grasp Assistive Devices—A Proof of Concept Study

“…For controlling the grasp type in those assistive devices, various invasive and non-invasive techniques, e.g., ECoG, EEG, etc. References [ 7 , 8 , 9 ] has already been used. However, the grasp aperture and orientation are still not possible to identify from the brain response alone.…”