The results are promising for the potential application of advanced feedback to close the control loop in sophisticated prosthetic systems.

Dexterous upper limb myoelectric prostheses can, to some extent, restore the motor functions lost after an amputation. However, ensuring the reliability of myoelectric control is still an open challenge. In this paper, we propose a classification method that exploits the regularity in muscle activation patterns (uniform scaling) across different force levels within a given movement class. This assumption leads to a simple training procedure, using training data collected at single contraction intensity for each movement class. The proposed method was compared to the widely accepted benchmark [linear discriminant analysis (LDA) classifier] using off-line and online evaluation. The off-line classification errors obtained with the new method were either lower or higher than LDA depending upon the chosen feature set. In the online evaluation, the new classification method was operated using amplitude-EMG features and compared to the state-of-the-art LDA classifier combined with the time domain feature set. The online evaluation was performed in 11 able-bodied and one amputee subject using a set of four functional tasks mimicking daily-life activities. The tasks assessed the dexterity (e.g., switching between functions) and robustness of control (e.g., handling heavy objects). With the new classification scheme, the amputee performed better in all functional tasks, whereas the able-bodied subjects performed significantly better in three out of four functional tasks. Overall, the novel method outperformed the state-of-the-art approach (LDA) while utilizing less training data and a smaller feature set. The proposed method is, therefore, a simple but effective and robust classification scheme, convenient for online implementation and clinical use.

In myoelectric prosthesis control, one of the hottest topics nowadays is enforcing simultaneous and proportional (s/p) control over several degrees of freedom. This problem is particularly hard and the scientific community has so far failed to provide a stable and reliable s/p control, effective in daily-life activities. In order to improve the reliability of this form of control, in this paper we propose on-the-fly knowledge composition, thereby reducing the burden of matching several patterns at the same time, and simplifying the task of the system. In particular, we show that using our method it is possible to dynamically compose a model by juxtaposing subsets of previously gathered (sample, target) pairs in real-time, rather than composing a single model in the beginning and then hoping it can reliably distinguish all patterns. Fourteen intact subjects participated in an experiment, where repetitive daily-life tasks (e.g. ironing a cloth) were performed using a commercially available dexterous prosthetic hand mounted on a splint and wirelessly controlled using a machine learning method. During the experiment, the subjects performed these tasks using myocontrol with and without knowledge composition and the results demonstrate that employing knowledge composition allowed better performance, i.e. reducing the overall task completion time by 30%.

This is the first online evaluation of a method integrating information from multiple on-board prosthesis sensors to modulate the output of a machine-learning-based myoelectric controller. The proposed scheme is general and presents a simple, non-invasive and cost-effective approach for improving the robustness of myoelectric control.