Controlling Robot Gripper Force By Transferring Human Forearm Stiffness Using Force Myography

Anvaripour, Mohammad; Saif, Mehrdad

doi:10.1109/mwscas.2018.8623937

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…where f min and f max are the minimum and maximum controllable force externally applied to the robot. Anvaripour and Saif (2018a) were defined the mapping parameter c as follow…”

Section: Estimating Forces Applied On the Robotmentioning

confidence: 99%

“…To find the corresponding value of force for use in the robot controller, the following mapping is defined

where f min and f max are the minimum and maximum controllable force externally applied to the robot. Anvaripour and Saif ( 2018a ) were defined the mapping parameter c as follow

where T 1 and T 2 are mapping parameters obtained experimentally.…”

Section: Collection and Processing Of Fmgmentioning

confidence: 99%

“…We also demonstrated that FMG data could be used to estimate the forearm muscle stiffness. Such an estimation was then applied to adjust the robot gripper force to handle different objects with the same gripping force as that of the human worker (Anvaripour and Saif, 2018a ). Further, we showed that the information provided with the FMG band along with the robot dynamics can be used to plan the trajectory of the robot during a shared task.…”

Section: Introductionmentioning

confidence: 99%

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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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“…where f min and f max are the minimum and maximum controllable force externally applied to the robot. Anvaripour and Saif (2018a) were defined the mapping parameter c as follow…”

Section: Estimating Forces Applied On the Robotmentioning

confidence: 99%

“…To find the corresponding value of force for use in the robot controller, the following mapping is defined

where f min and f max are the minimum and maximum controllable force externally applied to the robot. Anvaripour and Saif ( 2018a ) were defined the mapping parameter c as follow

where T 1 and T 2 are mapping parameters obtained experimentally.…”

Section: Collection and Processing Of Fmgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…applications, such as turning on-and-off a lamp or controlling a robotic device (Anvaripour & Saif, 2019). Also, FMG can be used to monitor the activity of daily living for exercise or rehabilitation purposes (Z. G. Xiao & Menon, 2019b, 2017aSadarangani et al, 2017;Stefanou et al, 2018).…”

Section: Public Interest Statementmentioning

confidence: 99%

Towards the investigation on the effect of the forearm rotation on the wrist FMG signal pattern using a high-density FMG sensing matrix

Xiao

Menon

2020

Cogent Engineering

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Force myography (FMG) is an emerging technique to predict extremity movement. The FMG signals captured near the wrist can be used to predict many hand gestures. However, the wrist FMG pattern of a gesture changes along with different forearm positions. It is difficult to accurately predict a gesture without knowing the effect of the forearm position on the wrist FMG pattern. To investigate such an effect, we developed a novel setup to register the pattern using the off-the-shelf force-sensing matrix to capture the change of the pattern during forearm rotation for two distinct hand gestures. Based on the collected data, we observed the wrist FMG patterns shifted in relation to the forearm orientation in some scenarios. We analyzed the results and examined the viability to use such a setup to facilitate future studies.

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“…In regression the data obtained from FMG is used to generate a continuous output signal. This method has been reported to track finger movements, hand/wrist force/torque, forearm stiffness, grasp intensity and knee joint angle [86,94,97,142,143]. In these methods data from each FSR was treated as a separate input feature and to predict desired task support vector machine, kernel ridge regression, random forest and NN techniques have been implemented.…”

Section: Regressionmentioning

confidence: 99%

FMG based upper limb motion detection methods, performance analysis and control of assistive exoskeletons

Islam¹

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Exoskeletons are wearable devices designed to assist humans according to their needs. Their applications can be found in rehabilitation, assistance and power augmentation. For assistive powered exoskeletons human motion intention detection is an important element for implementing assistive control strategies. While many methods of motion detection have been developed, however, there still exists many challenges i.e. high robustness, convenience, data repeatability and applicability for implementation on assistive powered exoskeletons. Therefore, new methods that can fulfill these requirements are needed.The aim of this thesis is to develop novel methods of motion intention detection for control of exoskeletons. The focus of this thesis is to analyze the performance of force myography (FMG) to detect upper limb movements and based on it develop control methods for upper limb assistive exoskeletons.In this thesis performance of FMG is analyzed by comparing it with sEMG. Motion detection accuracy and data repeatability were compared for detecting forearm motions i.e. forearm flexion, extension, pronation, supination and rest. The study showed the feasibility of FMG when implemented for assistive powered exoskeleton control.Exoskeleton control with FMG is another focus of this thesis. FMG is first used to control a soft exoskeleton by detecting dynamic hand gestures i.e. rest, opening, closing and grasping. This study addressed the challenges associated with object grasping task i.e. amount of training data, robust detection and assistance level determination. The influence of sensor placement on detection performance was also experimentally analyzed.Finally, FMG based control method for upper limb exoskeleton, i.e. elbow and shoulder joint, is presented in this thesis. A machine learning based algorithm is developed for determining assistance level during object carrying tasks by estimating the carried payload. The performance of the method is analyzed by testing on healthy subjects. Whereas, the results of physical assistance are verified by comparing the results of load carrying tasks with and without exoskeleton.This thesis contributes to the state-of-the-art of upper limb motion iniii tention detection using FMG. Studies verify that FMG, being accurate and a convenient method to interpret motion intention, has great potential for application of assistive exoskeletons. A contribution of this thesis is performance analysis of muscle activity detection methods that compares FMG and sEMG in terms of accuracy/repeatability. Another contribution is the novel methods for grasping and load carrying. The proposed techniques are able to reduce system complexity for convenient and robust use in actual environment.

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Controlling Robot Gripper Force By Transferring Human Forearm Stiffness Using Force Myography

Cited by 6 publications

References 14 publications

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

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

Towards the investigation on the effect of the forearm rotation on the wrist FMG signal pattern using a high-density FMG sensing matrix

FMG based upper limb motion detection methods, performance analysis and control of assistive exoskeletons

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