Original Research ARTICLE
Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: a Case Study.
- 1Simon Fraser University, Canada
- 2Barber Prosthetics Clinic, Canada
Various human machine interfaces (HMIs) are used to control prostheses such as robotic hands. One of the promising HMIs is Force Myography (FMG). Previous research has shown the potential for the use of high density FMG (HD-FMG) that can lead to higher accuracy of prosthesis control.
The more sensors used in an FMG controlled system, the more complicated and costlier the system becomes. This study proposes a design method that can produce powered prostheses with performance comparable to that of HD-FMG controlled systems using a fewer number of sensors. An HD-FMG apparatus would be used to collect information from the user only in the design phase. Channel selection would then be applied to the collected data to determine the number and location of sensors that are vital to performance of the device. This study assessed the use of multiple channel selection (CS) methods for this purpose.
In this case study, three datasets were used. These datasets were collected from force sensitive resistors embedded in the inner socket of a subject with transradial amputation. Sensor data were collected as the subject carried out five repetitions of six gestures. Collected data were then used to asses five CS methods: Sequential forward selection (SFS) with two different stopping criteria, minimum redundancy-maximum relevance (mRMR), genetic algorithm (GA), and Boruta.
Three out of the five methods (mRMR, GA, and Boruta) were able to decrease channel numbers significantly while maintaining classification accuracy in all datasets. Neither of them outperformed the other two in all datasets. However, GA resulted in the smallest channel subset in all three of the datasets.
The three selected methods were also compared in terms of stability (i.e. consistency of the channel subset chosen by the method as new training data were introduced or some training data were removed (Chandrashekar and Sahin, 2014)). Boruta and mRMR resulted in less instability compared to GA when applied to the datasets of this study.
This study shows feasibility of using the proposed design method that can produce prosthetic systems that are simpler than HD-FMG systems but have performance comparable to theirs.
Keywords: Force Myography (FMG), Gesture classification, prosthesis control, Robotic hand, high density FMG, Upper Limb Prosthesis, Channel selection (CS), Feature selection (FS)
Received: 04 Jul 2019;
Accepted: 29 Oct 2019.
Copyright: © 2019 Ahmadizadeh, Menon and Pousett. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Carlo Menon, Simon Fraser University, Burnaby, Canada, email@example.com