Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition.

ABSTRACT: Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation therapies. In the present study, cardinality is introduced and compared with traditional time-domain (Hudgins' set) and other recently proposed myoelectric features (for example, rough entropy). Cardinality was found to consistently outperform other features, including those that are more sophisticated and computationally expensive, despite variations

SUBMITTER: Ortiz-Catalan M 

PROVIDER: S-EPMC4625080 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature