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Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis

Lookup NU author(s): Dr Ravi Suppiah, Dr Noori KimORCiD, Dr Anurag Sharma, Dr Khalid Abidi



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 The Author(s). Published by IOP Publishing Ltd.A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately based on captured neuromuscular signals. Physiological signals such as Electromyography (EMG) and Electroencephalography (EEG) generated by neuromuscular activities contain intrinsic patterns for users’ particular actions. Such actions can generally be classified as motor states, such as Forward, Reverse, Hand-Grip, and Hand-Release. To classify these motor states truthfully, the signals must be captured and decoded correctly. This paper proposes a novel classification technique using a Fuzzy Inference System (FIS) and a Long Short-Term Memory (LSTM) network to classify the motor states based on EMG signals. Existing EMG signal classification techniques generally rely on features derived from data captured at a specific time instance. This typical approach does not consider the temporal correlation of the signal in the entire window. This paper proposes an LSTM with a Fuzzy Logic method to classify four major hand movements: forward, reverse, raise, and lower. Features associated with the pattern generated throughout the motor state movement were extracted by exploring published data within a given time window. The classification results can achieve a 91.3% accuracy for the 4-way action (Forward/Reverse/GripUp/RelDown) and 95.1% (Forward/Reverse Action) and 96.7% (GripUp/RelDown action) for 2-way actions. The proposed mechanism demonstrates high-level, human-interpretable results that can be employed in rehabilitation or medical-device industries.

Publication metadata

Author(s): Suppiah R, Kim N, Sharma A, Abidi K

Publication type: Article

Publication status: Published

Journal: Biomedical Physics and Engineering Express

Year: 2022

Volume: 8

Issue: 6

Online publication date: 08/11/2022

Acceptance date: 27/10/2022

Date deposited: 28/11/2022

ISSN (electronic): 2057-1976

Publisher: Institute of Physics


DOI: 10.1088/2057-1976/ac9e04

PubMed id: 36317231


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