Meta-Learning for Recalibration of EMG-Based Upper Limb Prostheses
An EMG-based upper limb prosthesis relies on a statistical pattern recognition system to map the EMG signal of residual forearm muscles into the appropriate hand movements. As the EMG signal changes each time the user puts the prosthesis on, an efficient method for prosthesis recalibration is needed. Here we show that meta-learning is a promising approach for achieving this aim. Furthermore, we show that meta-leaning can be used to recalibrate the prosthesis even when the examples of some movement types are missing in the target session.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
