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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.

(1) Motivation: Variations in the volume of the residual limb negatively impact various aspects of prosthesis use including the prosthetic socket fit. Although volume adjustment systems mitigate corresponding fit problems to some extent, some users still find the management of these systems challenging. With the ultimate goal of creating a feedback system that assists users with the management of their volume adjustment systems, this study demonstrates the feasibility of detecting variations in the volume of the residual limb. (2) Methods: Measurements of the interface force at the bottom of the prosthetic socket were used as indicators of variations in the volume of the residual limb. Force sensitive resistors (FSRs) were placed at the bottom of participants’ prosthetic sockets to monitor the interface limb–socket force as participants walked on a flat surface. Two phases of experiments were carried out: The first phase considered variations simulated by three prosthetic sock plies, established the feasibility of detecting variations in the volume of the limb based on the interface force, and further determined the locations at which the interface force could be used to detect variations in the limb’s volume. Having validated the effectiveness of the proposed method in the first phase, the second phase was carried out to determine the smallest detectable variation of the limb’s volume using the proposed method. In this phase, variations simulated by one and two prosthetic sock plies were considered. Four and three volunteers with transtibial amputations participated in the first and the second phases, respectively. (3) Results: Results of the first phase showed that an increase in the volume of the limb resulted in a decrease in the force measured at the distal location of the prosthetic sockets of all participants; however, the smallest detected variation could not be statistically confirmed.

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as reported by multiple studies, accuracies of the FSR-based pressure sensing systems decrease when sensors are bent to be positioned on a limb. This study proposes the use of regression-based methods for sensor calibration to address this problem. A sensor matrix was placed in a pressure chamber as the pressure was increased and decreased in a cyclic manner. Sensors' responses were assessed when the matrix was placed on a flat surface or on one of five curved surfaces with various curvatures. Three regression algorithms, namely linear regression (LR), general regression neural network (GRNN), and random forest (RF), were assessed. GRNN was selected due to its performance. Various error compensation methods using GRNN were investigated and compared to improve instability of sensors' responses. All methods showed improvements in results compared to the baseline. Developing a different model for each of the curvatures yielded the best results. This study proved the feasibility of using regression-based error compensation methods to improve the accuracy of mapping sensor readings to pressure values. This can improve the overall accuracy of FSR-based sensory systems used in prosthetic applications.