ADAPTIVE CONTROL METHOD AND SYSTEM FOR UPPER LIMB REHABILITATION ROBOT BASED ON GAME THEORY AND SURFACE ELECTROMYOGRAPHY (sEMG)
An adaptive control method and system for an upper limb rehabilitation robot based on a game theory and surface Electromyography (sEMG) is disclosed. A movement trajectory that a robot is controlled to run within a training time is designed during subject operation. An sEMG-based Back Propagation Neural Network (BPNN) muscle force estimation model establishes a nonlinear dynamic relationship between an sEMG signal and end force by constructing a three-layer neural network. A human-computer interaction system is analyzed by the game theory principle, and a role of the robot is deduced. The control rate between the robot and a subject is updated by Nash equilibrium, and adaptive weight factors of the robot and the subject are determined. The robot adaptively adjusts the training mode thereof according to a movement intention of the subject during operation and a weight coefficient obtained by the game theory principle..
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Patent| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Europäisches Patentamt - (2024) vom: 21. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
ZENG HONG [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2024-03-21, Last update posted on www.tib.eu: 2024-04-24, Last updated: 2024-04-26 |
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| Patentnummer: |
US2024096483 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA003014452 |
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| 245 | 1 | 0 | |a ADAPTIVE CONTROL METHOD AND SYSTEM FOR UPPER LIMB REHABILITATION ROBOT BASED ON GAME THEORY AND SURFACE ELECTROMYOGRAPHY (sEMG) |
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| 520 | |a An adaptive control method and system for an upper limb rehabilitation robot based on a game theory and surface Electromyography (sEMG) is disclosed. A movement trajectory that a robot is controlled to run within a training time is designed during subject operation. An sEMG-based Back Propagation Neural Network (BPNN) muscle force estimation model establishes a nonlinear dynamic relationship between an sEMG signal and end force by constructing a three-layer neural network. A human-computer interaction system is analyzed by the game theory principle, and a role of the robot is deduced. The control rate between the robot and a subject is updated by Nash equilibrium, and adaptive weight factors of the robot and the subject are determined. The robot adaptively adjusts the training mode thereof according to a movement intention of the subject during operation and a weight coefficient obtained by the game theory principle. | ||
| 650 | 4 | |a G16H: Healthcare informatics, i.e. information and communication technology [ict] specially adapted for the handling or processing of medical or healthcare data | |
| 650 | 4 | |a A61H: Physical therapy apparatus, e.g. devices for locating or stimulating reflex points in the body; artificial respiration; massage; bathing devices for special therapeutic or hygienic purposes or specific parts of the body (electrotherapy, magnetotherapy, radiation therapy, ultrasound therapy a61n) | |
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