On the estimation of isometric wrist/forearm torque about three axes using Force Myography

Sakr, Maram; Menon, Carlo

doi:10.1109/biorob.2016.7523730

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…SVR is the counterpart of SVM in the regression domain and both SVR and SVM are based on the same core principle, which is to identify the supported vector from the input data and form the model. SVR was used in the work of Castellini’s group to predict force exerted by the fingers [35,68], the work of Menon’s group to predict force exertion from the hand and dynamic finger movements [33,69,70], and in the work of Englehard’s group to predict upper limb movements with a high-density force-sensing matrix [48,71].…”

Section: Fmg Processing Methodsmentioning

confidence: 99%

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A Review of Force Myography Research and Development

Xiao

Menon

2019

Sensors

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101

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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.

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Section: Fmg Processing Methodsmentioning

confidence: 99%

“…It’s architecture and training method are almost identical to ANN, with the exception of the output layer, which does not have an activation function. GRNN was used in the work of Sakr et al, [40,69] and Kadkhodayan et al [70].…”

Section: Fmg Processing Methodsmentioning

confidence: 99%

A Review of Force Myography Research and Development

Xiao

Menon

2019

Sensors

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101

107

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“…FSRs are small, easy to use, low-cost and repeatable, which is essential for any wearable sensor. As a contemporary technique to the sEMG, FMG signals has proven their potential in similar researches of finger and gesture recognition, isometric grasp/hand force recognition, prosthetic upper limb control, rehabilitation applications, and assistive solutions to activities in daily life (ADLs) of elders [17][18][19][20][21][22]. Surprisingly, this technique is found comparable and sometimes better than the sEMG in those applications.…”

Section: Introductionmentioning

confidence: 99%

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

Zakia

Menon

2020

Sensors

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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.

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“…Force Myography (FMG) is a technique to quantify changes in the volume of a limb resulting from muscle contractions and relaxations (Xiao and Menon, 2014 ). This biosignal has been employed in a variety of applications including gesture recognition, control of exoskeletons, prostheses, and linear actuators, and estimation of user-applied forces to manipulate planar linear actuators (Xiao et al, 2014 ; Cho et al, 2016 ; Sakr and Menon, 2016a , b , 2017 , 2018 ; Jiang et al, 2017 ; Sadarangani and Menon, 2017 ; Zakia and Menon, 2020 ). Force myography from upper-limbs could be collected using lightweight, compact, and unobtrusive bands wrapped around wrist, forearm, and/or upper arm, which makes it an attractive technique for developing wearables.…”

Section: Introductionmentioning

confidence: 99%

FMG- and RNN-Based Estimation of Motor Intention of Upper-Limb Motion in Human-Robot Collaboration

et al. 2020

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Research on human-robot interactions has been driven by the increasing employment of robotic manipulators in manufacturing and production. Toward developing more effective human-robot collaboration during shared tasks, this paper proposes an interaction scheme by employing machine learning algorithms to interpret biosignals acquired from the human user and accordingly planning the robot reaction. More specifically, a force myography (FMG) band was wrapped around the user's forearm and was used to collect information about muscle contractions during a set of collaborative tasks between the user and an industrial robot. A recurrent neural network model was trained to estimate the user's hand movement pattern based on the collected FMG data to determine whether the performed motion was random or intended as part of the predefined collaborative tasks. Experimental evaluation during two practical collaboration scenarios demonstrated that the trained model could successfully estimate the category of hand motion, i.e., intended or random, such that the robot either assisted with performing the task or changed its course of action to avoid collision. Furthermore, proximity sensors were mounted on the robotic arm to investigate if monitoring the distance between the user and the robot had an effect on the outcome of the collaborative effort. While further investigation is required to rigorously establish the safety of the human worker, this study demonstrates the potential of FMG-based wearable technologies to enhance human-robot collaboration in industrial settings.

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On the estimation of isometric wrist/forearm torque about three axes using Force Myography

Cited by 12 publications

References 20 publications

A Review of Force Myography Research and Development

A Review of Force Myography Research and Development

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

FMG- and RNN-Based Estimation of Motor Intention of Upper-Limb Motion in Human-Robot Collaboration

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