Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis.

Background: Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training.

Towards robust HD EMG pattern recognition: reducing electrode displacement effect using structural similarity.

Even small changes of electrode recording sites after training a classifier heavily influence robustness and usability of traditional pattern recognition-based myoelectric control schemes. This effect occurs during donning and doffing of the prosthesis or when changing the arm position and generally leads to a significant decrease of classification accuracy. On the other hand, image representations taken from high density electromyographic (EMG) signals offer high spatial resolution and only seem to change slightly during electrode shift, preserving most structural information.

Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes.

Pattern recognition of myoelectric signals in upper-limb prosthesis control has been subject to intense research for several years. However, few systems have yet been successfully clinically implemented. One possible explanation for this discrepancy is that published reports mostly focus on classification accuracy of myoelectric signals recorded under laboratory conditions as the metric for the system's performance.

Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array.

The robustness and usability of pattern recognition based myoelectric control systems degrade significantly if the sensors are displaced during usage. This effect inevitably occurs during donning, doffing or using an upper-limb prosthesis over a longer period of time. Electrode shift has been previously studied but remains an unsolved problem.

Fluctuating emg signals: investigating long-term effects of pattern matching algorithms.

In this paper, we investigate the behavior of state-of-the-art pattern matching algorithms when applied to electromyographic data recorded during 21 days. To this end, we compare the five classification techniques k-nearest-neighbor, linear discriminant analysis, decision trees, artificial neural networks and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize ten different hand movements.