quantifying-arms-legs-contributions-during-repetitive-electrically

Quantifying Arms and Legs Contributions during Repetitive Electrically-Assisted Sit-To-Stand Exercise in Paraplegics: A Pilot Study

Execution of Sit-to-Stand (SitTS) in incomplete spinal cord injury (SCI) patients involves motor function in both upper and lower extremities. The use of ...

Universiti Malaya

The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation. 

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression.

A mechanomyography muscle contraction (MC) sensor, affixed to the skin surface, was used to quantify muscle tension during repetitive functional electrical stimulation (FES)-evoked isometric rectus femoris contractions to fatigue in individuals with spinal cord injury (SCI). Nine persons with motor complete SCI were seated on a commercial muscle dynamometer that quantified peak torque and average torque outputs, while measurements from the MC sensor were simultaneously recorded. MC-sensor-predicted measures of dynamometer torques, including the signal peak (SP) and signal average (SA), were highly associated with isometric knee extension peak torque (SP: r = 0.91,  < 0.0001), and average torque (SA: r = 0.89,  < 0.0001), respectively. Bland-Altman (BA) analyses with Lin's concordance () revealed good association between MC-sensor-predicted peak muscle torques (SP;  = 0.91) and average muscle torques (SA;  = 0.89) with the equivalent dynamometer measures, over a range of FES current amplitudes. The relationship of dynamometer torques and predicted MC torques during repetitive FES-evoked muscle contraction to fatigue were moderately associated (SP: r = 0.80,  < 0.0001; SA: r = 0.77;  < 0.0001), with BA associations between the two devices fair-moderate (SP;  = 0.70: SA;  = 0.30). These findings demonstrated that a skin-surface muscle mechanomyography sensor was an accurate proxy for electrically-evoked muscle contraction torques when directly measured during isometric dynamometry in individuals with SCI. The novel application of the MC sensor during FES-evoked muscle contractions suggested its possible application for real-world tasks (e.g., prolonged sit-to-stand, stepping,) where muscle forces during fatiguing activities cannot be directly measured.

Mechanomyography and Torque during FES-Evoked Muscle Contractions to Fatigue in Individuals with Spinal Cord Injury.

A quasi-experimental single-blinded study.

Effects of Graded Exercises Integrated with Education on Physical Fitness, Exercise SelfEfficacy, and Activity Levels in People with Spinal Cord Injury: A Quasi-Experimental Study Protocol.

Article	Year
Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression. Sensors (Basel, Switzerland)\| PubMed ID: 27447638	2016
Mechanomyography and Torque during FES-Evoked Muscle Contractions to Fatigue in Individuals with Spinal Cord Injury. Sensors (Basel, Switzerland)\| PubMed ID: 28708068	2017
Effects of Graded Exercises Integrated with Education on Physical Fitness, Exercise SelfEfficacy, and Activity Levels in People with Spinal Cord Injury: A Quasi-Experimental Study Protocol. Asian spine journal\| PubMed ID: 30866621	2019

Nazirah Hasnan

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