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Cited by 412

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A Human--Exoskeleton Interface Utilizing Electromyography

Fleischer, Christian, Hommel, GÜnter·2008·IEEE Transactions on Robotics

Abstract

This paper presents a human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.

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Metadata

Authors
Fleischer, C., & Hommel, G.
Venue
IEEE Transactions on Robotics
Published
2008-08
Volume
24
Issue
4
Pages
872-882
ISSN
  • 1552-3098 (ISSN-L)
  • 1941-0468
OpenAlex
W2162438232
Cited by
412
References
2
FWCI
6.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Human--Exoskeleton Interface Utilizing Electromyography. https://app.cochat.ai/papers/10.1109/tro.2008.926860
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Fleischer, C., & Hommel, G. (2008). A Human--Exoskeleton Interface Utilizing Electromyography. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2008.926860

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.