Differentiating Variations in Thumb Position From Recordings of the Surface Electromyogram in Adults Performing Static Grips, a Proof of Concept Study

Abstract

Hand gesture and grip formations are produced by the muscle synergies arising between extrinsic and intrinsic hand muscles and many functional hand movements involve repositioning of the thumb relative to other digits. In this study we explored whether changes in thumb posture in able-body volunteers can be identified and classified from the modulation of forearm muscle surface-electromyography (sEMG) alone without reference to activity from the intrinsic musculature. In this proof-of-concept study, our goal was to determine if there is scope to develop prosthetic hand control systems that may incorporate myoelectric thumb-position control. Healthy volunteers performed a controlled-isometric grip task with their thumb held in four different opposing-postures. Grip force during task performance was maintained at 30% maximal-voluntary-force and sEMG signals from the forearm were recorded using 2D high-density sEMG (HD-sEMG arrays). Correlations between sEMG amplitude and root-mean squared estimates with variation in thumb-position were investigated using principal-component analysis and self-organizing feature maps. Results demonstrate that forearm muscle sEMG patterns possess classifiable parameters that correlate with variations in static thumb position (accuracy of 88.25 ± 0.5% anterior; 91.25 ± 2.5% posterior musculature of the forearm sites). Of importance, this suggests that in transradial amputees, despite the loss of access to the intrinsic muscles that control thumb action, an acceptable level of control over a thumb component within myoelectric devices may be achievable. Accordingly, further work exploring the potential to provide myoelectric control over the thumb within a prosthetic hand is warranted.

Explication of Extrinsic Forearm Muscles On the Classification of Thumb Position Using High-Density Surface Electromyogram

“…Due to its mathematical simplicity and good performance, time-domain (TD) features are commonly used [18]. TD features are determined based on the amplitude of the signals and do not require any complicated measurement and frequently used TD feature is Root Mean Square (RMS) [2,17] and mean absolute value [15,18], while frequency-domain (FD) characteristics are based on the frequency range and calculated based on the Fourier transformation and frequently features used are mean frequency [16,19] and median frequency [16,20]. The results of the study conducted by Siddiqi and Sidek [17] showed TD analysis produces higher accuracy to differentiate different finger attitudes than FD.…”

“…Previous studies have used various classifiers such as Neural Network (NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA). As the result, all the classifiers performance show accuracy above 89%, however, there is not a single best classifier as it depends on the data set [2,26]. The results of different classification algorithms are compared to find which algorithm produces effective results.…”

“…Injury or loss of function of the thumb can severely limit overall hand function and movements [1]. A study by Park et al [2] reported that thumb corresponds to at least 50% of hand functions. The groups of muscles that control the movements of fingers and overall hand grip strength can be divided into two categories namely extrinsic and intrinsic muscles.…”

Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

“…Multi-stream networks have better generalization capabilities. The high variability of sEMG and the lack of existing data limit the application of gesture recognition technology Aranceta-Garza and Conway, 2019). In future work, high-density sEMG (Chen et al, 2020) and multiple information fusion will be the direction of dynamic gesture recognition research.…”

Influence of pitching grip on shoulder and elbow injuries in junior baseball players, focusing on hand length and the metacarpophalangeal joint angle

“…Therefore, each finger must be in the extended position and the thumb is forced to grip the side of the ball. Non-sports studies have reported that the position of the thumb changes the muscle activity of the flexor and extensor muscles of the forearm in simple grasping movements 11) . Different methods of holding the ball in baseball are affected by hand length and ball size and may influence the position of the thumb and the metacarpophalangeal joint (MP) angle.…”