Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications

Abstract

The development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs.

Permodelan Inversi Peredam Magnet-Reologi Berbasis Jaringan Saraf Tiruan Untuk Sistem Kendali

“…Parameter yang dimaksud adalah jumlah lapisan tersembunyi, jumlah neuron, dan fungsi aktivasi. Untuk proses pembelajaran/training, algoritma yang digunakan adalah Levenberg-Marquardtz yang telah terbukti kemampuannya dalam proses optimasi pencarian nilai parameter di dalam arsitektur atau struktur JST [17].…”

Artificial Neural Network Accuracy Optimization Using Transfer Function Methods on Various Human Gait Walking Environments

“…EMG features have been calculated in other studies [17], [19], [20]. Some studies used 5 time-domain features [17] and other used 14 time domain features [19], meanwhile, another incorporated 11 EMG features with time domain and frequency domain combination [20]. The gap between this study's analysis and another study lies in the various transfer function combinations used, and the 12 features consist of both time-domain and frequency-domain features.…”

“…A study found that complicated data acquisition protocol can increase the ANN accuracy [15], the study used the sigmoid function as one of the layers. EMG features have been calculated in other studies [17], [19], [20]. Some studies used 5 time-domain features [17] and other used 14 time domain features [19], meanwhile, another incorporated 11 EMG features with time domain and frequency domain combination [20].…”