EMG based classification for pick and place task

Abstract

The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.

Pattern recognition of EMG signals for low level grip force classification

“…The customized cylindrical gripper was capable to detect the grip force applied by the fingers during fingertip grasping of the objects [42]. A space was allocated in the gripper to place different weights in the gripper as shown in figure 2.…”

“…The duration of the task was fixed to 13-15 s by fixing the time period of each section of the task. This has been explained in detail in previous study [42]. The pick and place action was performed for 4 levels of the weights.…”

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

“…The pick and place activity is a compilation of several smaller activities, also called the phases. Among these phases, is the phase of gripping and lifting [ 33 ]. This phase can be further divided into two broader classes, named as the transient phase and the steady phase [ 31 ].…”