Faculty of Design and Creative Technologies (Te Ara Auaha)

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https://hdl.handle.net/10292/10758
The Faculty of Design and Creative Technologies - Te Ara Auaha is comprised of four schools: The School of Future Environments - Huri Te Ao, the School of Art and Design - Te Kura Toi a Hoahoa, the School of Communication Studies - Te Kura Whakapāho and the School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau. It also has Institutes, Centres and Labs across the Arts and Sciences in a mix that blends the traditional and the new, praxis and theory.

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Browsing Faculty of Design and Creative Technologies (Te Ara Auaha) by Author "Abbaspour, S"

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    Evaluation of Surface EMG-based Recognition Algorithms for Decoding Hand Movements
    (Springer Verlag, 2020) Abbaspour, S; Lindén, M; Gholamhosseini, H; Naber, A; Ortiz-Catalan, M
    Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.
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