Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition
Journal article, 2015

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 in sampling frequency, time window length, contraction dynamics, type, and number of movements (single or simultaneous), and classification algorithms. Provided that the signal resolution is kept between 12 and 14 bits, cardinality improves myoelectric pattern recognition for the prediction of motion volition. This technology is instrumental for the rehabilitation of amputees and patients with motor impairments where myoelectric signals are viable. All code and data used in this work is available online within BioPatRec.

Prosthetic control

Bioelectric signal processing

Cardinality

Myoelectric pattern recognition

EMG

Electromyography

Author

Max Jair Ortiz Catalan

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

Frontiers in Neuroscience

1662-4548 (ISSN)

Vol. 9 OCT 416

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Other Medical Engineering

DOI

10.3389/fnins.2015.00416

More information

Created

10/7/2017