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.
Highlights
In forming hand gestures and grip patterns, activation of both extrinsic and intrinsic hand muscles is necessary (Maier and Hepp-Reymond, 1995)
As coordinated motions of the digits are driven by user intention executed through muscle synergies there must be embedded within the EMG activation patterns a predictable outcome of what actions an individual’s hand is being commanded to perform
The proposition to control prosthetic hands by identifying muscle synergies or patterns is not new, but there has been very little research investigating if sEMG pattern recognition approaches have the potential to extract information on intended thumb actions as part of grip formation
Summary
In forming hand gestures and grip patterns, activation of both extrinsic and intrinsic hand muscles is necessary (Maier and Hepp-Reymond, 1995). The intrinsic hand muscles that act on the thumb play a critical role in determining both opposing grip strength and thumb positioning. In healthy subjects, these muscles are aided by the actions of the deep extrinsic hand musculature (Figure 1). The residual forearm muscles within the remaining limb segment are accessible and usable for myoelectric prosthetic control. This creates a challenging control problem, as without direct access to the hand muscles and a lack in specificity in sampling, the potential for intuitive control of prosthetic thumb is significantly compromised
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