2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) 2018
DOI: 10.1109/mwscas.2018.8623937
|View full text |Cite
|
Sign up to set email alerts
|

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

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
5
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
3
3

Relationship

1
5

Authors

Journals

citations
Cited by 6 publications
(5 citation statements)
references
References 14 publications
0
5
0
Order By: Relevance
“…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%
See 2 more Smart Citations
“…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%
See 1 more Smart Citation
“…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%
“…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%