RESUMEN
BACKGROUND: Human beings regularly walk over even and uneven surfaces during their daily activities. A human being with lower limb disability needs an exoskeleton to walk independently. However, walking surface irregularities increase the risk of falling of exoskeleton users. This falling tendency can be minimized by balancing the exoskeleton on irregular surface profiles against the gait cycle variation. Gait variation is studied using quality EMG signals obtained from the gastrocnemius and hamstring muscle activity during uneven surface walking. OBJECTIVE: The present study compares the activity of hamstring and gastrocnemius muscles during walking on a treadmill, utilizing both even and uneven planes. METHODS: Integrated electromyography signals from eight healthy male subjects are collected while walking on a treadmill, even and uneven planes. Muscle activity variation on these planes is studied using two-way ANOVA with replications. RESULTS: The results show that hamstring muscle activity registers a sound variation in swing phase but has no variation in stance phase over all three planes, whereas gastrocnemius muscle activity changes between swing and stance phases over even and uneven planes during forward walking. CONCLUSIONS: The results illustrate that the gait cycle variation depends on surface irregularities which indicates the importance of surface consideration.
Asunto(s)
Marcha , Músculos Isquiosurales/fisiología , Músculo Esquelético/fisiología , Adulto , Electromiografía , Humanos , Masculino , Propiedades de Superficie , CaminataRESUMEN
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. CONCLUSION: 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.