A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model.

Sujiao Li, Lei Zhang, Qiaoling Meng, Hongliu Yu

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

4 authors.

Sujiao LiInstitute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.

Lei ZhangInstitute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.ORCID 0009-0003-2683-1080

Qiaoling MengInstitute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.ORCID 0000-0002-0240-4004

Hongliu YuInstitute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.

Funding

National Natural Science Foundation of China 61903255

6 · The paper itself

Abstract

Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles' EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the

Indexed as

active torque prediction modelreal-time controlrehabilitation exoskeletonsEMG

Identifiers

PMID38136032

DOI10.3390/bioengineering10121441

PMCPMC10741095

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.