Study on the force myography sensors placement for robust hand force estimation

Sakr, Maram; Menon, Carlo

doi:10.1109/smc.2017.8122807

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…23–26 The results indicate that performance continuously downgrades as the time difference between training and testing day increases. On the other hand, FMG as an alternative to detect upper and lower limb muscle activities has been used in different applications with healthy subject 27–37 and with stroke/amputated subjects. 38,39…”

Section: Introductionmentioning

confidence: 99%

A comparative study of motion detection with FMG and sEMG methods for assistive applications

Islam

Waris

Kamavuako

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

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Section: Introductionmentioning

confidence: 99%

A comparative study of motion detection with FMG and sEMG methods for assistive applications

Islam

Waris

Kamavuako

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

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show abstract

“…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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“…In addition, the impact of sensors placement and the spatial coverage of FMG measurements were studied. Sakr and Menon ( 2017 ) was a preliminary investigation for the best placement of the FMG bands on the arm for 3-DoF hand force prediction. In this paper, an extensive study for the best placement within four landmarks on the arm for FMG bands for improving the accuracy of 3-DoF force and torque prediction and 6-DoF force/torque prediction was provided.…”

Section: Introductionmentioning

confidence: 99%

Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography

2019

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Hand force estimation is critical for applications that involve physical human-machine interactions for force monitoring and machine control. Force Myography (FMG) is a potential technique to be used for estimating hand force/torque. The FMG signals reflect the volumetric changes in the arm muscles due to muscle contraction or expansion. This paper investigates the feasibility of employing force-sensing resistors (FSRs) worn on the arm to measure the FMG signals for isometric force/torque estimation. Nine participants were recruited in this study and were asked to exert isometric force along three perpendicular axes, torque about the same three axes, and force and torque simultaneously. During the tests, the isometric force and torque were measured using a 6-degree-of-freedom (DoF) (i.e., force in three axes and torque around the same axes) load cell for ground truth labels whereas the FMG signals were recorded using a total number of 60 FSRs, which were embedded into four bands worn on the different locations of the arm. A two-stage regression strategy was employed to enhance the performance of the FMG bands, where three regression algorithms including general regression neural network (GRNN), support vector regression (SVR), and random forest regression (RF) models were employed, respectively, in the first stage and GRNN was used in the second stage. Two cases were considered to explore the performance of the FMG bands in estimating: (1) 3-DoF force and 3-DoF torque at once and (2) 6-DoF force and torque. In addition, the impact of sensor placement and the spatial coverage of FMG measurements were studied. This preliminary investigation demonstrates promising potential of FMG to estimate multi-DoF isometric force/torque. Specifically, R 2 accuracies of 0.83 for the 3-DoF force, 0.84 for 3-DoF torque, and 0.77 for the combination of force and torque (6-DoF) regressions were obtained using the four bands on the arm in cross-trial evaluation.

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Study on the force myography sensors placement for robust hand force estimation

Cited by 13 publications

References 20 publications

A comparative study of motion detection with FMG and sEMG methods for assistive applications

A comparative study of motion detection with FMG and sEMG methods for assistive applications

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

Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography

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