Exploratory Evaluation of the Force Myography (FMG) Signals Usage for Admittance Control of a Linear Actuator

2020

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Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control.

Section: Discussionsupporting

confidence: 78%

Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

Zakia

2020

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“…This layer consists of two neurons: a numerator neuron that is the summation of weighted target values; and the denominator neuron which is the summation of weight values. The mathematical representation is the following [36,40]:…”

Section: Regression Methodsmentioning

confidence: 99%

“…The three regression algorithms that were assessed for this study are amongst the most vastly used for regression purposes for FSR signals used in biomedical applications [8,[35][36][37]. Linear machine learning algorithms are commonly used for various types of signals for a multitude of applications due to their capability of prediction and their computational efficiency [9,35,38,39].…”

Section: Introductionmentioning

confidence: 99%

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

Ahmadizadeh

2019

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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.

“…While most of the works related to human-machine-interface were aiming at applications that utilized an open-loop control strategy, researchers also started to investigate FMG in scenarios that involved dynamic interaction between the user’s hand and external robotic devices. For instance, an exploratory study by Sakr et al showed that FMG could be used for admittance control applications [40]. In this study, a handler connected to a linear actuator could react to the applied force predicted from FMG sensors located on both the distal and proximal ends of the forearm.…”

Section: Fmg Signal Acquisitionmentioning

confidence: 99%

A Review of Force Myography Research and Development

Xiao

2019

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101

107

Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.