Phantom-Mobility-Based Prosthesis Control in Transhumeral Amputees Without Surgical Reinnervation: A Preliminary Study

Abstract

Transhumeral amputees face substantial difficulties in efficiently controlling their prosthetic limb, leading to a high rate of rejection of these devices. Actual myoelectric control approaches make their use slow, sequential and unnatural, especially for these patients with a high level of amputation who need a prosthesis with numerous active degrees of freedom (powered elbow, wrist, and hand). While surgical muscle-reinnervation is becoming a generic solution for amputees to increase their control capabilities over a prosthesis, research is still being conducted on the possibility of using the surface myoelectric patterns specifically associated to voluntary Phantom Limb Mobilization (PLM), appearing naturally in most upper-limb amputees without requiring specific surgery. The objective of this study was to evaluate the possibility for transhumeral amputees to use a PLM-based control approach to perform more realistic functional grasping tasks. Two transhumeral amputated participants were asked to repetitively grasp one out of three different objects with an unworn eight-active-DoF prosthetic arm and release it in a dedicated drawer. The prosthesis control was based on phantom limb mobilization and myoelectric pattern recognition techniques, using only two repetitions of each PLM to train the classification architecture. The results show that the task could be successfully achieved with rather optimal strategies and joint trajectories, even if the completion time was increased in comparison with the performances obtained by a control group using a simple GUI control, and the control strategies required numerous corrections. While numerous limitations related to robustness of pattern recognition techniques and to the perturbations generated by actual wearing of the prosthesis remain to be solved, these preliminary results encourage further exploration and deeper understanding of the phenomenon of natural residual myoelectric activity related to PLM, since it could possibly be a viable option in some transhumeral amputees to extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.

Vibrotactile Feedback for a Person with Transradial Amputation and Visual Loss: A Case Report

“…Sensory feedback from the prosthetic fingers and hand improved the manual dexterity of the myoelectric multi-grip prosthetic hand by improving grasping ability and coordination in a person with transradial amputation and concomitant vision loss. Due to the complex hybrid and myoelectric multi-joint control approaches in transhumeral amputations or shoulder disarticulations [60], the usability of FEELIX could be limited in such cases. Additionally, since people with visual loss have enhanced proprioception, haptic judgment, and passive tactile acuity compared to sighted individuals, the effect of providing sensory feedback from prosthetic hands may not be as pronounced in sighted people [9].…”

Phantom Movement Training Without Classifier Performance Feedback Improves Mobilization Ability While Maintaining EMG Pattern Classification

“…They were both computed before (Pre) and after (Post) training, for each type of movement. For the classification of EMG patterns, we used a linear discriminant analysis (LDA) classifier which is commonly used in pattern recognition algorithms (e.g., [13], [21], [30], [34]). Using the abovementioned half-cycles, each movement type was assigned to a different class.…”

“…Using the abovementioned half-cycles, each movement type was assigned to a different class. For each class, the following features were computed from the EMG: the root mean square (RMS envelope), the first 4 autoregressive coefficients, the zero crossings, the wavelength and the sample entropy [13], [21]. Because of the extended duration of sample entropy computation, a 512-ms sliding analysis window with a 128-ms overlap between successive windows was used.…”

“…As each contraction pattern is specific to a given type of phantom movement, surface EMG signals are particularly informative about the type of PHM being performed [13], [21]. PR has been used to identify the type of performed movement, in above-elbow amputees in particular, which eventually allows intuitive control of myoelectric prostheses with many DoF [13], [21]. Thus, phantom-based prosthesis control is a promising noninvasive technique that might improve both ergonomics and performance of myoelectric prostheses.…”

And Yet It Moves: What We Currently Know about Phantom Arm Movements

“…Electrode placement is shown for each patient. Adapted from Jarrassé and others (2018). (B) (Left) EMG signals obtained from eight electrode channels during the execution of eight phantom movements.…”