Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 23; no. 4; pp. 618 - 627
• Main Authors: Hahne, Janne M., Dahne, Sven, Hwang, Han-Jeong, Muller, Klaus-Robert, Parra, Lucas C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2015
• Subjects: Adaptation models; Calibration; Closed-loop-control; co-adaptation; Computer Systems; Electrodes; Electromyography; Electromyography - instrumentation; Electromyography - methods; Feature extraction; Hand; Humans; Learning; Least-Squares Analysis; Limb Deformities, Congenital - rehabilitation; Muscle Contraction; myoelectric control; Prostheses and Implants; Prosthesis Design; prosthetic hand; Psychomotor Performance; Real-time systems; real-time-learning; regression; simultaneous control; Training
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2015.2401134

Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation or congenital limb-deficiency who need to (re)learn how to generate suitable muscle contractions, this open-loop process may not be effective. We present a closed-loop real-time learning scheme in which both the user and the machine learn simultaneously to follow a common target. Experiments with ten able-bodied individuals show that this co-adaptive closed-loop learning strategy leads to significant performance improvements compared to a conventional open-loop training paradigm. Importantly, co-adaptive learning allowed two individuals with congenital deficiencies to perform simultaneous 2-D proportional control at levels comparable to the able-bodied individuals, despite having to a learn completely new and unfamiliar mapping from muscle activity to movement trajectories. To our knowledge, this is the first study which investigates man-machine co-adaptation for regression-based myoelectric control. The proposed training strategy has the potential to improve myographic prosthetic control in clinically relevant settings.