Basic study on combined motion estimation using multichannel surface EMG signals

Online Natural Myocontrol of Combined Hand and Wrist Actions Using Tactile Myography and the Biomechanics of Grasping

Objective: Despite numerous recent advances in the field of rehabilitation robotics, simultaneous, and proportional control of hand and/or wrist prostheses is still unsolved. In this work we concentrate on myocontrol of combined actions, for instance power grasping while rotating the wrist, by only using training data gathered from single actions. This is highly desirable since gathering data for all possible combined actions would be unfeasibly long and demanding for the amputee. Approach: We first investigated physiologically feasible limits for muscle activation during combined actions. Using these limits we involved 12 intact participants and one amputee in a Target Achievement Control test, showing that tactile myography, i.e., high-density force myography, solves the problem of combined actions to a remarkable extent using simple linear regression. Since real-time usage of many sensors can be computationally demanding, we compare this approach with another one using a reduced feature set. These reduced features are obtained using a fast, spatial first-order approximation of the sensor values. Main results: By using the training data of single actions only, i.e., power grasp or wrist movements, subjects achieved an average success rate of 70.0% in the target achievement test using ridge regression. When combining wrist actions, e.g., pronating and flexing the wrist simultaneously, similar results were obtained with an average of 68.1%. If a power grasp is added to the pool of actions, combined actions are much more difficult to achieve (36.1%). Significance: To the best of our knowledge, for the first time, the effectiveness of tactile myography on single and combined actions is evaluated in a target achievement test. The present study includes 3 DoFs control instead of the two generally used in the literature. Additionally, we define a set of physiologically plausible muscle activation limits valid for most experiments of this kind.

Using spatial features for classification of combined motions based on common spatial pattern

Motion recognition is an important application of electromyography (EMG) analysis. While discrete motions such as hand open, hand close and wrist pronation have been extensively investigated, studies on combined motions involving two or more degrees of freedom (DOFs) are relatively few and the classification accuracy of the combined motions reported in previous studies is barely satisfactory. To improve the accuracy of the combined motion recognition, common spatial pattern (CSP) was employed in this study to extract spatial features. 18 forearm motion classes, consisted of 8 discrete motions and 10 combined motions, were classified by the proposed method. Our results showed that the accuracy rate of CSP features was 96.3%, which outperformed the commonly used time-domain (TD) features by 2.4% and TD combined with auto-regression coefficients (TDAR) by 0.6%. Moreover, CSP features cost noticeable much less time than TDAR and quite less time than TD in testing. These results suggest that CSP features could be a better feature set for multi-DOF myoelectric control than conventional features.

The LET Procedure for Prosthetic Myocontrol: Towards Multi-DOF Control Using Single-DOF Activations

Simultaneous and proportional myocontrol of dexterous hand prostheses is to a large extent still an open problem. With the advent of commercially and clinically available multi-fingered hand prostheses there are now more independent degrees of freedom (DOFs) in prostheses than can be effectively controlled using surface electromyography (sEMG), the current standard human-machine interface for hand amputees. In particular, it is uncertain, whether several DOFs can be controlled simultaneously and proportionally by exclusively calibrating the intended activation of single DOFs. The problem is currently solved by training on all required combinations. However, as the number of available DOFs grows, this approach becomes overly long and poses a high cognitive burden on the subject. In this paper we present a novel approach to overcome this problem. Multi-DOF activations are artificially modelled from single-DOF ones using a simple linear combination of sEMG signals, which are then added to the training set. This procedure, which we named LET (Linearly Enhanced Training), provides an augmented data set to any machine-learning-based intent detection system. In two experiments involving intact subjects, one offline and one online, we trained a standard machine learning approach using the full data set containing single- and multi-DOF activations as well as using the LET-augmented data set in order to evaluate the performance of the LET procedure. The results indicate that the machine trained on the latter data set obtains worse results in the offline experiment compared to the full data set. However, the online implementation enables the user to perform multi-DOF tasks with almost the same precision as single-DOF tasks without the need of explicitly training multi-DOF activations. Moreover, the parameters involved in the system are statistically uniform across subjects.

Control of upper limb prostheses: terminology and proportional myoelectric control-a review

The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.