A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control: Validation in Real-Life Scenarios on Amputees

Abstract

Functional replacement of upper limbs by means of dexterous prosthetic devices remains a technological challenge. While the mechanical design of prosthetic hands has advanced rapidly, the human-machine interfacing and the control strategies needed for the activation of multiple degrees of freedom are not reliable enough for restoring hand function successfully. Machine learning methods capable of inferring the user intent from EMG signals generated by the activation of the remnant muscles are regarded as a promising solution to this problem. However, the lack of robustness of the current methods impedes their routine clinical application. In this study, we propose a novel algorithm for controlling multiple degrees of freedom sequentially, inherently proportionally and with high robustness, allowing a good level of prosthetic hand function. The control algorithm is based on the spatial linear combinations of amplitude-related EMG signal features. The weighting coefficients in this combination are derived from the optimization criterion of the common spatial patterns filters which allow for maximal discriminability between movements. An important component of the study is the validation of the method which was performed on both able-bodied and amputee subjects who used physical prostheses with customized sockets and performed three standardized functional tests mimicking daily-life activities of varying difficulty. Moreover, the new method was compared in the same conditions with one clinical/industrial and one academic state-of-the-art method. The novel algorithm outperformed significantly the state-of-the-art techniques in both subject groups for tests that required the activation of more than one degree of freedom. Because of the evaluation in real time control on both able-bodied subjects and final users (amputees) wearing physical prostheses, the results obtained allow for the direct extrapolation of the benefits of the proposed method for the end users. In conclusion, the method proposed and validated in real-life use scenarios, allows the practical usability of multifunctional hand prostheses in an intuitive way, with significant advantages with respect to previous systems.

Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping

“…Therefore, we expect that the results would not be significantly different in naïve persons with amputation, and even more so, the general conclusions regarding the importance of incidental feedback in prosthesis control. In experienced people with amputation, the baseline performance is likely to be even better than suggested here, due to extensive prosthesis use [35]. …”

Classification of Phantom Finger, Hand, Wrist, and Elbow Voluntary Gestures in Transhumeral Amputees With sEMG

“…Having the patients training themselves to perform in a repeatable manner their phantom limb movements could possibly stabilize the associated sEMG patterns and maximize the interclass distance [32], reduce cognitive fatigue and finally improve the robustness of this control approach, as recenlty shown in [33] with forearm amputees for control with their phantom limb. We do also believe that, possibly with some control methods different from pattern recognition like the ones reviewed in [34] or recent alternatives like [35] and [36], these phantom limb mobilities related myoelectric activities could be used in transhumeral amputees to extend their control abilities over prosthetics and in some cases be a sufficient alternative to surgical procedures.…”

Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses

“…Although significant progress has been made in the field of articulated prostheses [4], control interfaces [5] [6] and control algorithms [7], the sensory feedback component and its practical effectiveness in activities of daily living remains an unresolved issue. Yet it stands to reason to believe that such feedback should improve the control of the prosthesis in the daily use [2] [8].…”