Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Abstract

Background: 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. Motivation: 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. Methods: 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. Results: 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. Conclusion: 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.

Towards Management of Residual Limb Volume: Monitoring the Prosthetic Interface Pressure to Detect Volume Fluctuations—A Feasibility Study

“…Signals used in this method can be potentially acquired throughout extended periods of time. FSR signals are easy to process, making this method computationally efficient [28,29]. Another advantage of this method is that it does not require direct contact with the skin and is less prone to errors due to environmental changes compared to some of the alternative methods.…”

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

“…Again, Sakr et al showed the possibility of predicting force in dynamic conditions by using FSRs worn around the arm [150]. Ahmadizadeh et al explored the application of feature selection to three high-density FMG datasets in order to reduce its dimensionality and, at the same time, achieve the same performance but with lower cost and complexity [151]. Xiao et al proposed a novel FMG system, consisting of a strap embedded with eight FSRs, to detect different forearm positions for controlling a custom-made forearm pronation/supination exoskeleton [152].…”

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

“…Over the last few years, multiple non-invasive control methods of prosthetic hands have been introduced and investigated; for example, surface electromyography (sEMG) (Fougner et al, 2012 ; Farina et al, 2014 ; Krasoulis et al, 2017 ; Pizzolato et al, 2017 ; Ameri et al, 2018 ; Li et al, 2018 ; Leone et al, 2019 ; Olsson et al, 2019 ; Junior et al, 2020 ), electroneurography (ENG) (Cloutier and Yang, 2013 ; Paul et al, 2018 ), mechanomyography (MMG) (Xiloyannis et al, 2015 ; Wilson and Vaidyanathan, 2017 ), and force myography (FMG) (Rasouli et al, 2015 ; Sadeghi and Menon, 2018 ; Ahmadizadeh et al, 2019 ). In particular, sEMG is a non-invasive technique for measuring the electrical activity of groups of muscles on the skin surface, which makes it a simple and straightforward way to allow the user to actively control the prosthesis.…”