Lifting activity assessment using surface electromyographic features and neural networks

Lifting Activity Assessment Using Kinematic Features and Neural Networks

“…By differentiating the marker's position after a 4-Hz low-pass filter (Butterworth 3rd order), the velocities were obtained. The onset and the end point of the lifting task were defined, respectively, as the time point at which the velocity of the crate marker on the vertical axis exceeded the value of 0.025 m/s [30] and as the time point when the crate marker velocity fell below the same velocity threshold. Kinematic and kinetic data underwent a time normalization procedure so as to be reduced to 101 samples using a polynomial procedure.…”

“…, 8) ( Figure 1) constituted as in the following: In terms of network architecture, we tested nine different network architectures defined by the combination of number of HL and number of N in each layer. Particularly, we tested ANNs with one, two or three HL and different number of neurons in each HL: N was set to 12, 20 and 50 for the first HL (N HL1 ) and, in the other HL, if defined, was N/2 for the second (N HL2 ) and N/3 (N HL3 ) for the third [30].…”

“…A confusion matrix 3 × 3 was calculated for each ANNs considering the actual LI and the one estimated by the ANN on the randomly extracted testing set. Then, the mean confusion matrix was obtained for each network topology and each SET and a performance parameter (P) was calculated as the mean (%) of the elements on the diagonal of these confusion matrices, where 100% indicates the absence of misclassifications [30].…”

“…Among the instrumental and quantitative approaches proposed to evaluate different tasks and classify the biomechanical risk, those based on artificial neural networks (ANNs) are particularly promising [27][28][29][30]. An ANN is a model, constituted by elementary processing units (neurons) interconnected in different topologies, mimicking the behavior of real neuronal networks.…”

“…Amplitude and coordination parameters calculated from sEMG data (maximum, average rectified value, root mean square, muscle coactivation, [32,33] have been shown to be correlated to the compression and shear forces and moments at L5-S1 joint, and able to detect the risk levels. Recently, Varrecchia and colleagues [30] used ANNs for mapping surface sEMG features on lifting index (LI) levels for biomechanical risk estimation, defined based on the RNLE. They have shown that LI levels during lifting tasks can be reliably estimated through multi-layered ANNs by using a set of combined sEMG time and frequency features obtained only from the erector spinae muscle.…”