Details

Autor(en) / Beteiligte

• Hahne, Janne M.
• Dahne, Sven
• Hwang, Han-Jeong
• Muller, Klaus-Robert
• Parra, Lucas C.

Titel

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

Ist Teil von

• IEEE transactions on neural systems and rehabilitation engineering, 2015-07, Vol.23 (4), p.618-627

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2015

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.