Shoulder movements are not considered for electromyography-based pattern classification control, due to the difficulty to manufacture three-degrees-of-freedom shoulder prostheses. This paper aims at exploring the feasibility of classifying up to nine shoulder movements by processing surface electromyography signals from eight trunk muscles. Experimenting with different pattern recognition methods, two classifiers were developed, considering six different combinations of window sizes and increments, and three feature sets for each channel. Applying linear discriminant analysis the best performance was obtained on a window length of 500 ms associated to temporal increments of 62 ms. This setting yielded a 100% accuracy for recognizing four movements, and progressively degraded to 92% for nine movements. Using neural networks, higher accuracy was obtained in particular in the 9-class problem. Finally, the signals from the eight channels were analyzed in order to check the possibility to reduce the number of acquisition channels.
Abstract-Artificial arms for shoulder disarticulation need a high number of degrees of freedom to be controlled. In order to control a prosthetic shoulder joint, an intention detection system based on surface electromyography (sEMG) pattern recognition methods was proposed and experimentally investigated. Signals from eight trunk muscles that are generally preserved after shoulder disarticulation were recorded from a group of eight normal subjects in nine shoulder positions. After data segmentation, four different features were extracted (sample entropy, cepstral coefficients of the 4th order, root mean square and waveform length) and classified by means of linear discriminant analysis. The classification accuracy was 92.1% and this performance reached 97.9% after reducing the positions considered to five classes. To reduce the computational cost, the two channels with the least discriminating information were neglected yielding to a classification accuracy diminished by just 4.08%.
Some Virtual Reality applications are based on the use of haptic interfaces for a more intuitive and realistic manipulation of the virtual objects. Typically, the haptic devices have a fixed position in the real space, and their working space is rather limited. As a consequence, there are locations in the virtual space that are out of the working space of the haptic device, and thus cannot be reached by users during the virtual objects manipulation. The paper describes a multimodal navigation modality based on the integrated use of various and low cost interaction devices that can be operated by a user taking into account that one of his hands is engaged for the manipulation of the haptic device. Therefore, we have decided to implement the user interface by using the Nintendo® Wii Remote™ and the BalanceBoard™, which can be operated by the user using the other hand and his feet. The navigation modality has been integrated and tested in a Virtual Reality application for the virtual manual assembly of mechanical components. A preliminary validation of the application has been performed by an expert user with the aim of identifying major usability and performance issues by using the heuristic evaluation method.
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