Toward Intuitive Prosthetic Control: Solving Common Issues Using Force Myography, Surface Electromyography, and Pattern Recognition in a Pilot Case Study

Ahmadizadeh, Chakaveh; Merhi, Lukas-Karim; Pousett, Brittany; Sangha, Sohail; Menon, Carlo

doi:10.1109/mra.2017.2747899

Cited by 51 publications

(49 citation statements)

References 18 publications

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“…Powered prostheses can provide users with means to gain back some of their upper limb functionality and subsequently increase their self-sufficiency. Reliability and intuitiveness of prostheses control play important roles in user experience and are amongst factors lack of which can lead to prostheses abandonment (Biddiss and Chau, 2007;Ahmadizadeh et al, 2017). Issues concerning these factors are commonly addressed through advancements in robustness of human machine interfaces (HMIs) used for prostheses control and through the use of pattern recognition for more intuitive control.…”

Section: Application Backgroundmentioning

confidence: 99%

“…Recent studies have demonstrated FMG to be a promising HMI for upper limb prosthesis control (Cho et al, 2016;Radmand et al, 2016;Ahmadizadeh et al, 2017;Jiang et al, 2017;Sadeghi Chegani and Menon, 2018). FMG monitors changes in volumetric pattern of user's forearm and detects intentions of the user based on these changes (Rasouli et al, 2016).…”

Section: Application Backgroundmentioning

confidence: 99%

“…Pattern recognition plays an important role in enhancement of this aspect of user experience in prostheses applications. Various classification methods have been assessed for FMG controlled prostheses, some of the commonly used ones of which are linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbors (KNN) (Naik et al, 2015;Ahmadizadeh et al, 2017). Amongst these, LDA is one of the most widely used classifiers due to its capability in separating different classes of gestures and also its computational efficiency (Cho et al, 2016;Radmand et al, 2016;Xiao and Menon, 2017a).…”

Section: Application Backgroundmentioning

confidence: 99%

“…Amongst these, LDA is one of the most widely used classifiers due to its capability in separating different classes of gestures and also its computational efficiency (Cho et al, 2016;Radmand et al, 2016;Xiao and Menon, 2017a). A study by Ahmadizadeh et al compared performance of the three aforementioned classifiers for gesture classification using FMG data and determined LDA to be the classifier of choice for their study (Ahmadizadeh et al, 2017).…”

Section: Application Backgroundmentioning

confidence: 99%

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

Ahmadizadeh

Pousett²,

Menon

2019

Front. Bioeng. Biotechnol.

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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.

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Section: Application Backgroundmentioning

confidence: 99%

Section: Application Backgroundmentioning

confidence: 99%

Section: Application Backgroundmentioning

confidence: 99%

Section: Application Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

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

Ahmadizadeh

Pousett²,

Menon

2019

Front. Bioeng. Biotechnol.

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“…The pressure profile at the interface of the prosthetic socket and the residual limb contains important information that can be used for various applications in the field of prostheses. Some of the most common prosthetic applications for which the use of this pressure map has been explored include control of powered prostheses using Force Myography (FMG) [1][2][3] and prosthetic fitting [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

Ahmadizadeh

Menon

2019

Sensors

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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.

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Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics

2022

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Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics would make a promising alternative to conventional rigid devices, which could potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible humanmachine interfaces are summarized by recent and renown applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

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Toward Intuitive Prosthetic Control: Solving Common Issues Using Force Myography, Surface Electromyography, and Pattern Recognition in a Pilot Case Study

Cited by 51 publications

References 18 publications

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

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

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics

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