Offline Accuracy: A Potentially Misleading Metric in Myoelectric Pattern Recognition for Prosthetic Control
Paper in proceedings, 2015

Offline accuracy has been the preferred performance measure in myoelectric pattern recognition (MPR) for the prediction of motion volition. In this study, different metrics relating the fundamental binary prediction outcomes were analyzed. Our results indicate that global accuracy is biased by 1) the unbalanced number of possible true positive and negative outcomes, and 2) the almost perfect specificity and negative predicted value, which were consistently found across algorithms, topologies, and movements (individual and simultaneous). Therefore, class-specific accuracy is advisable instead. Additionally, we propose the use of precision (positive predictive value) and sensitivity (recall) as a complement to accuracy to better describe the discrimination capabilities of MPR algorithms, as these consider the effect of false predictions. However, all the studied offline metrics failed to predict real-time decoding, and therefore real-time testing continue to be necessary to truly evaluate the clinical usability of MPR.

Author

Max Jair Ortiz Catalan

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

Rouhani Rouhani

Rickard Brånemark

University of Gothenburg

Bo Håkansson

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

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

1557170X (ISSN)

1140-1143 7318567

Subject Categories

Orthopedics

DOI

10.1109/EMBC.2015.7318567

PubMed

26736467

ISBN

978-1-4244-9271-8

More information

Created

10/7/2017