An early force prediction control scheme using multimodal sensing of electromyography and digit force signals.

Different grasping gestures result in the change of muscular activity of the forearm muscles. Similarly, the muscular activity changes with a change in grip force while grasping the object. This change in muscular activity, measured by a technique called Electromyography (EMG) is used in the upper limb bionic devices to select the grasping gesture. Previous research studies have shown gesture classification using pattern recognition control schemes. However, the use of EMG signals for force manipulation is less focused, especially during precision grasping. In this study, an early predictive control scheme is designed for the efficient determination of grip force using EMG signals from forearm muscles and digit force signals. The optimal pattern recognition (PR) control schemes are investigated using three different inputs of two signals: EMG signals, digit force signals and a combination of EMG and digit force signals. The features extracted from EMG signals included Slope Sign Change, Willison Amplitude, Auto Regressive Coefficient and Waveform Length. The classifiers used to predict force levels are Random Forest, Gradient Boosting, Linear Discriminant Analysis, Support Vector Machines, k-nearest Neighbors and Decision Tree. The two-fold objectives of early prediction and high classification accuracy of grip force level were obtained using EMG signals and digit force signals as inputs and Random Forest as a classifier. The earliest prediction was possible at 1000 ms from the onset of the gripping of the object with a mean classification accuracy of 90 % for different grasping gestures. Using this approach to study, an early prediction will result in the determination of force level before the object is lifted from the surface. This approach will also result in better biomimetic regulation of the grip force during precision grasp, especially for a population facing vision deficiency.