title: Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.
abstract: BACKGROUND
  Estimation of elbow dynamics has been the object of numerous investigations.
  
  
  OBJECTIVE
  In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals.
  
  
  METHODS
  Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm.
  
  
  RESULTS
  The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347.
  
  
  CONCLUSIONS
  The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.
authors.forename: Retheep
authors.initials: R
authors.lastname: Raj
authors.forename: K S
authors.initials: KS
authors.lastname: Sivanandan
mesh_terms: D000328:Adult; D000465:Algorithms; D001132:Arm; D001696:Biomechanical Phenomena; D004550:Elbow; D004551:Elbow Joint / Q000502:physiology*; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*; D016571:Neural Networks, Computer; D016059:Range of Motion, Articular
keywords: Levenberg-marquardt algorithm; NARX model; Surface electromyography; elbow angular velocity; elbow joint angular displacement; integrated EMG; neural network; time domain features; zero crossing