An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject
Journal article, 2018

The functionality of upper limb prostheses can be improved by intuitive control strategies that use bioelectric signals measured at the stump level. One such strategy is the decoding of motor volition via myoelectric pattern recognition (MPR), which has shown promising results in controlled environments and more recently in clinical practice. Moreover, not much has been reported about daily life implementation and real-time accuracy of these decoding algorithms. This paper introduces an alternative approach in which MPR allows intuitive control of four different grips and open/close in a multifunctional prosthetic hand. We conducted a clinical proof-of-concept in activities of daily life by constructing a self-contained, MPR-controlled, transradial prosthetic system provided with a novel user interface meant to log errors during real-time operation. The system was used for five days by a unilateral dysmelia subject whose hand had never developed, and who nevertheless learned to generate patterns of myoelectric activity, reported as intuitive, for multi-functional prosthetic control. The subject was instructed to manually log errors when they occurred via the user interface mounted on the prosthesis. This allowed the collection of information about prosthesis usage and real-time classification accuracy. The assessment of capacity for myoelectric control test was used to compare the proposed approach to the conventional prosthetic control approach, direct control. Regarding the MPR approach, the subject reported a more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. This paper represents an alternative to the conventional use of MPR, and this alternative may be particularly suitable for a certain type of amputee patients. Moreover, it represents a further validation of MPR with dysmelia cases.

electromyogram (emg)

dysmelia

Prosthetic control

myoelectric pattern recognition (MPR)

assessment of capacity for myoelectric control (ACMC)

Author

Enzo Mastinu

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

Johan Ahlberg

Royal Institute of Technology (KTH)

Eva Lendaro

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

Liselotte Hermansson

Örebro University Hospital

Bo Håkansson

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

Max Jair Ortiz Catalan

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

IEEE Journal of Translational Engineering in Health and Medicine

2168-2372 (ISSN)

Vol. 6

Subject Categories

Biomedical Laboratory Science/Technology

Control Engineering

Computer Science

DOI

10.1109/JTEHM.2018.2811458