TY - JOUR
T1 - Myoelectric control of artificial limbsis there a need to change focus? [In the Spotlight]
AU - Jiang, Ning
AU - Dosen, Strahinja
AU - Muller, Klaus Robert
AU - Farina, Dario
N1 - Funding Information:
This work is also contributed to by Bernhard Graimann and Hans Dietl from Otto Bock HealthCare. The opinion expressed is based on the authors’ work through financing by the German Ministry for Education and Research (BMBF) via the Bernstein Focus Neurotechnology (BFNT) Göttingen under grant 01GQ0810; BFNT Berlin via grant 01GQ0850; European Commission via the Industrial Academia Partnerships and Pathways (IAPP) under grant 251555 (AMYO) and grant 286208 (MYOSENS); European Research Council (ERC) via the ERC Advanced grant DEMOVE (267888); and the World Class University Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology (grant R31-10008).
PY - 2012
Y1 - 2012
N2 - The basic concept of myoelectric control and the state of the art in both industry and academia is discussed. Myoelectric control has a great potential for improving the quality of life of persons with limb deficiency. The pattern classification approach for myoelectric control is based on the assumption that there exist distinguishable and repeatable signal patterns among different types of muscular activations. A pattern classification myoelectric controller usually consists of three main steps, segmentation, feature extraction, and classification. The main problem with the pattern classification for myoelectric control is that it inherently leads to a control scheme that is substantially different from the natural control. Academic research has focused on refining classification accuracy, creating a gap between he academia and the industry state of the art. This gap could be filled by addressing the specific needs of intuitive myoelectric control and system robustness.
AB - The basic concept of myoelectric control and the state of the art in both industry and academia is discussed. Myoelectric control has a great potential for improving the quality of life of persons with limb deficiency. The pattern classification approach for myoelectric control is based on the assumption that there exist distinguishable and repeatable signal patterns among different types of muscular activations. A pattern classification myoelectric controller usually consists of three main steps, segmentation, feature extraction, and classification. The main problem with the pattern classification for myoelectric control is that it inherently leads to a control scheme that is substantially different from the natural control. Academic research has focused on refining classification accuracy, creating a gap between he academia and the industry state of the art. This gap could be filled by addressing the specific needs of intuitive myoelectric control and system robustness.
UR - http://www.scopus.com/inward/record.url?scp=85032751739&partnerID=8YFLogxK
U2 - 10.1109/MSP.2012.2203480
DO - 10.1109/MSP.2012.2203480
M3 - Review article
AN - SCOPUS:85032751739
VL - 29
SP - 148
EP - 152
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
SN - 1053-5888
IS - 5
M1 - 6279589
ER -