title: 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.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Amsuess
authors.forename: Peter
authors.initials: P
authors.lastname: Goebel
authors.forename: Bernhard
authors.initials: B
authors.lastname: Graimann
authors.forename: Dario
authors.initials: D
authors.lastname: Farina
mesh_terms: D000465:Algorithms; D000672:Amputation Stumps / Q000503:physiopathology*; D000674:Amputees / Q000534:rehabilitation*; D001186:Artificial Limbs; D004576:Electromyography / Q000379:methods*; D019544:Equipment Failure Analysis; D025461:Feedback, Physiological; D006225:Hand; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011474:Prosthesis Design; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity