Abstract
Motivation: Hand amputations can dramatically affect the quality of life of a person. Researchers are developing surface electromyography and machine learning solutions to control dexterous and robotic prosthetic hands, however long computational times can slow down this process.Objective: This paper aims at creating a fast signal feature extraction algorithm that can extract widely used features and allow researchers to easily add new ones.Methods: PaWFE (Parallel Window Feature Extractor) extracts the signal features from several time windows in parallel. The MATLAB code is publicly available and supports several time domain and frequency features. The code was tested and benchmarked using 1,2,4,8,16,32, and 48 threads on a server with four Xeon E7- 4820 and 128 GB RAM using the first 5 datasets of the Ninapro database, that are recorded with different acquisition setups.Results: The parallel time window analysis approach allows to reduce the computational time up to 20 times when using 32 cores, showing a very good scalability. Signal features can be extracted in few seconds from an entire data acquisition and in <100 ms from a single time window, easily reducing of up to over 15 times the feature extraction procedure in comparison to traditional approaches. The code allows users to easily add new signal feature extraction scripts, that can be added to the code and on the Ninapro website upon request.Significance: The code allows researchers in machine learning and biosignals data analysis to easily and quickly test modern machine learning approaches on big datasets and it can be used as a resource for real time data analysis too.
Highlights
Hand amputations can dramatically affect the quality of life of a person
The parallel signal feature extraction code presented in this paper aims at reproducing the feature extraction part of the signal classification procedure described by Englehart and Hudgins (2003) using parallel multiple cores, in order to reduce computational time
The method has several advantages in comparison to other approaches: it does not require segmentation of the sEMG data; it allows delivering a continuous stream of class decisions to the prosthesis; it allows substantial gains in classification accuracy and response time; it allows natural control without interruption and it requires minimal storage capacity for real time approaches, which is an important factor in embedded control systems
Summary
The combination of surface electromyography and machine learning is a promising solution to control dexterous robotic hands. Worldwide research groups are working to make machine learning algorithms capable to analyze electromyography data for hand prosthetics in real time and robustly. Real time control experiments provide the best evaluation of prosthesis usability (Hargrove et al, 2007; Scheme and Englehart, 2011). These studies require the interaction of the user with the control system, so they do not allow to compare new analysis procedures (unless the entire study is repeated) (Pizzolato et al, 2017). Offline experiments allow to test and compare new methods but can take several weeks of computational time
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