Neural evidence for functional roles of tactile and visual feedback in the application of myoelectric prosthesis

Eliciting Force and Slippage in Upper Limb Amputees Through Transcutaneous Electrical Nerve Stimulation (TENS)

Upper limb amputation severely affects the quality of life of individuals. Therefore, developing closed-loop upper-limb prostheses would enhance the sensory-motor capabilities of the prosthetic user. Considering design priorities based on user needs, the restoration of sensory feedback is one of the most desired features. This study focuses on employing Transcutaneous Electrical Nerve Stimulation (TENS) as a non-invasive somatotopic stimulation technique for restoring somatic sensations in upper-limb amputees. The aim of this study is to propose two encoding strategies to elicit force and slippage sensations in transradial amputees. The former aims at restoring three different levels of force through a Linear Pulse Amplitude Modulation (LPAM); the latter is devoted to elicit slippage sensations through Apparent Moving Sensation (AMS) by means of three different algorithms, i.e. the Pulse Amplitude Variation (PAV), the Pulse Width Variation (PWV) and Inter-Stimulus Delay Modulation (ISDM). Amputees had to characterize perceived sensations and to perform force and slippage recognition tasks. Results demonstrates that amputees were able to correctly identify low, medium and high levels of force, with an accuracy above the 80% and similarly, to also discriminate the slippage moving direction with a high accuracy above 90%, also highlighting that ISDM would be the most suitable method, among the three AMS strategies to deliver slippage sensations. It was demonstrated for the first time that the developed encoding strategies are effective methods to somatotopically reintroduce in the amputees, by means of TENS, force and slippage sensations.

Closed-Loop Force Control by Biorealistic Hand Prosthesis With Visual and Tactile Sensory Feedback

The ability of a novel biorealistic hand prosthesis for grasp force control reveals improved neural compatibility between the human-prosthetic interaction. The primary purpose here was to validate a virtual training platform for amputee subjects and evaluate the respective roles of visual and tactile information in fundamental force control tasks. We developed a digital twin of tendon-driven prosthetic hand in the MuJoCo environment. Biorealistic controllers emulated a pair of antagonistic muscles controlling the index finger of the virtual hand by surface electromyographic (sEMG) signals from amputees' residual forearm muscles. Grasp force information was transmitted to amputees through evoked tactile sensation (ETS) feedback. Six forearm amputees participated in force tracking and holding tasks under different feedback conditions or using their intact hands. Test results showed that visual feedback played a predominant role than ETS feedback in force tracking and holding tasks. However, in the absence of visual feedback during the force holding task, ETS feedback significantly enhanced motor performance compared to feedforward control alone. Thus, ETS feedback still supplied reliable sensory information to facilitate amputee's ability of stable grasp force control. The effects of tactile and visual feedback on force control were subject-specific when both types of feedback were provided simultaneously. Amputees were able to integrate visual and tactile information to the biorealistic controllers and achieve a good sensorimotor performance in grasp force regulation. The virtual platform may provide a training paradigm for amputees to adapt the biorealistic hand controller and ETS feedback optimally.

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.