Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

Abstract

To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

Online pattern recognition of lower limb movements based on sEMG signals and its application in real-time rehabilitation training

“…However, when faced with such problems, researchers usually choose to increase the training sample data size. For example, Fan et al [34] used traditional methods to collect a large amount of lower limb EMG signal data from subjects through wired collectors, but the recognition rate still cannot be stable at over 90%, and it increases the workload of data collection in the early stage and increases the data dimension. Faced with the problem of large data collection and long classifier construction time, some researchers have adopted the transfer learning method [17,18] to reduce the amount of data, which adds a small amount of data from new testers to the original classifier to construct a new classifier.…”

“…A more complex data yields a greater AE, whereas a more regular data yields a smaller AE. The AE [34] is calculated as follows:…”

AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

“…[ 22 ] Therefore, muscle activity measurement is a popular approach for capturing and recognizing human lower‐limb motions without interfering with joint movement. Presently, the standard muscle activity measurement techniques are electromyography (EMG) [ 23 , 24 ] and surface EMG (sEMG) with using noninvasive electrodes, [ 13 , 25 , 26 , 27 ] both of which measure the electrical signals generated by muscle contraction. These two techniques have demonstrated exceptional recognition accuracy for several common lower‐limb motions.…”

“…These two techniques have demonstrated exceptional recognition accuracy for several common lower‐limb motions. [ 24 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] Moreover, EMG and sEMG have also been combined with inertial sensors to obtain more information. [ 34 , 35 , 36 , 37 ] sEMG, in particular, is more widely used than EMG due to its noninvasiveness and ease of use.…”

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

“…The most important application of action recognition is video surveillance [ 5 ]. Governments use this application for intelligence gathering, reducing crime rate, for security purposes [ 6 ], or even crime investigation [ 7 ]. The main motivation of growing research in HAR is due to its use in video surveillance applications [ 8 ].…”