Neural Network-Based Muscle Torque Estimation Using Mechanomyography During Electrically-Evoked Knee Extension and Standing in Spinal Cord Injury

Frontiers in Neurorobotics
Muhammad Afiq DzulkifliNazirah Hasnan

Abstract

This study sought to design and deploy a torque monitoring system using an artificial neural network (ANN) with mechanomyography (MMG) for situations where muscle torque cannot be independently quantified. The MMG signals from the quadriceps were used to derive knee torque during prolonged functional electrical stimulation (FES)-assisted isometric knee extensions and during standing in spinal cord injured (SCI) individuals. Three individuals with motor-complete SCI performed FES-evoked isometric quadriceps contractions on a Biodex dynamometer at 30° knee angle and at a fixed stimulation current, until the torque had declined to a minimum required for ANN model development. Two ANN models were developed based on different inputs; Root mean square (RMS) MMG and RMS-Zero crossing (ZC) which were derived from MMG. The performance of the ANN was evaluated by comparing model predicted torque against the actual torque derived from the dynamometer. MMG data from 5 other individuals with SCI who performed FES-evoked standing to fatigue-failure were used to validate the RMS and RMS-ZC ANN models. RMS and RMS-ZC of the MMG obtained from the FES standing experiments were then provided as inputs to the developed ANN models to calculate the ...Continue Reading

References

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Citations

Sep 29, 2019·Biomedizinische Technik. Biomedical Engineering·Musfirah Abd AzizMuhammad Afiq Dzulkifli
Apr 19, 2020·Biomedizinische Technik. Biomedical Engineering·Jannatul NaeemManfred Bijak
Jan 5, 2021·Biomedical Engineering Online·Raphael UwamahoroIndra Devi Subramaniam

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