Abstract
BackgroundIn this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach.MethodsSeven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics.ResultsBoth methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA.ConclusionsThese results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees.
Highlights
In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol
We propose and test a nonlinear mapping of EMG based on autoencoders (AEN), which exploits the advantage of unsupervised learning and the power of non-linear regression
A strong linear relation was found between completion time (CT) and index of difficulty (ID) for both control approaches, supporting the suitability of applying the Fitts’ Law test (Fig. 4)
Summary
We propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach. The commercially available products still mainly rely on a simple direct and sequential control. This control strategy offers robust and reliable handling of the prosthetic in daily life, but it allows limited recovery of functionality and requires high cognitive load by the user [1, 2]. Major advances in myocontrol have been made with pattern recognition approaches. These methods are based on the assumption that sufficiently distinguishable patterns can be observed in the EMG recordings during different motions. With state of the art pattern recognition methods, the classification accuracy exceeds > 95% when discriminating > 10 classes [3]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
