Cyclostationary Modeling of Surface Electromyography Signal During Gait Cycles and Its Application for Cerebral Palsy Diagnosis

Cerebral palsy(CP) is a group of permanent movement disorders that appear in early childhood.The electromyography(EMG) signal analysis and the gait analysis are two most commonly used methods in the clinic. In this paper, a cyclostationary model of the EMG signal is proposed. The model can combine the aforementioned two methods. The EMG signal acquired during the gait cycles is assumed to be cyclostationary due to the physiological characteristics of the EMG signal production. Then, the spectral correlation density is used to analyze the cyclic frequency(corresponding to the gait cycles) and spectral frequency(the frequency of EMG signal) in a waterfall representation of the two kinds of frequencies. The experiments show that the asymptomatic(normal) subjects and symptomatic subjects(with CP) can be distinguished from the spectral correlation density in a range of cyclic frequencies.

A Deep Neural Network Model for Upper Limb Swing Pattern to Control an Active Bionic Leg

Leg amputations are common in accidents and diseases.The present active bionic legs use Electromyography(EMG)signals in lower limbs(just before the location of the amputation)to generate active control signals.The active control with EMGs greatly limits the potential of using these bionic legs because most accidents and diseases cause severe damages to tissues/muscles which originates EMG signals.As an alternative,the present research attempted to use an upper limb swing pattern to control an active bionic leg.A deep neural network(DNN)model is implemented to recognize the patterns in upper limb swing,and it is used to translate these signals into active control input of a bionic leg.The proposed approach can generate a full gait cycle within 1082 milliseconds,and it is comparable to the normal(a person without any disability)1070 milliseconds gait cycle.

Analysis of the Biotribological Behavior for the Stainless Steel/Polyethylene Contact Using a Knee Prosthesis Simulator

In this work, a friction and wear simulator was used to reproduce the Anterior-Posterior （AP） sliding and the Flex- ion-Extension （FE） rotation generated in the knee joint during human gait cycle. We chose to simplify the contact geometry between the Total Knee Arthroplasty （TKA） femoral component and tibial insert. A 304L stainless steel cylinder which replaces the femoral component was loaded onto a flat High Density Polyethylene （HDPE） block which replaces the tibial insert. The tribological behavior of the considered contact was analyzed by tracking the number of cycles, the friction coefficient, the roughness of the wear track on HDPE, the HDPE weight loss and the damage mechanisms. The friction coefficient shows a gradual increase with the number of cycles for both AP and FE kinematics. The evolution of friction coefficient with the number of cycles is not affected by the value of the imposed normal load in the case of AP sliding. For the FE rotation, decreased friction coefficient is obtained when the imposed normal load increases. For both considered AP and FE kinematics, the roughness of the wear track on the HDPE is not affected by the imposed normal load. It shows a progressive decrease when the number of cycles increases. The wear of HDPE obeys the Archard law and the wear coefficient increases with the normal force. For a given value of normal load, the obtained wear coefficient for the AP sliding is larger than that obtained for FE rotation. A predominant adhesive wear mechanism was identified for both AP and FE kinematics. Under the same normal load, damage development in terms of plastic deformation, micro-cracking and debonding is more pronounced for the AP sliding if compared with the FE rotation. For a given kinematics, the damage severity increases with the normal load. This finding is in good agreement with the predicted values of the wear coefficient according to the Archard law.