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An Intuitive Formulation of the Human Arm Active Endpoint Stiffness

Authors
Wu, YuqiangZhao, FeiKim, WansooAjoudani, Arash
Issue Date
Sep-2020
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Keywords
Human factors; Physical human–robot collaboration; Robot adaptation and learning
Citation
SENSORS, v.20, no.18, pp.1 - 15
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
20
Number
18
Start Page
1
End Page
15
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/105812
DOI
10.3390/s20185357
ISSN
1424-8220
Abstract
In this work, we propose an intuitive and real-time model of the human arm active endpoint stiffness. In our model, the symmetric and positive-definite stiffness matrix is constructed through the eigendecomposition Kc = V DVT, where V is an orthonormal matrix whose columns are the normalized eigenvectors of Kc, and D is a diagonal matrix whose entries are the eigenvalues of Kc . In this formulation, we propose to construct V and D directly by exploiting the geometric information from a reduced human arm skeleton structure in 3D and from the assumption that human arm muscles work synergistically when co-contracted. Through the perturbation experiments across multiple subjects under different arm configurations and muscle activation states, we identified the model parameters and examined the modeling accuracy. In comparison to our previous models for predicting human active arm endpoint stiffness, the new model offers significant advantages such as fast identification and personalization due to its principled simplicity. The proposed model is suitable for applications such as teleoperation, human–robot interaction and collaboration, and human ergonomic assessments, where a personalizable and real-time human kinodynamic model is a crucial requirement.
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ERICA 공학대학 (DEPARTMENT OF ROBOT ENGINEERING)
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