Human Locomotion Activity and Speed Recognition Using Electromyography Based Features

Sorkhabadi, Seyed Mostafa Rezayat; Chinimilli, Prudhvi Tej; Gaytan-Jenkins, Daniel; Zhang, Wenlong

doi:10.1109/wearracon.2019.8719626

Cited by 6 publications

(1 citation statement)

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“…As surface electromyography (sEMG) could directly represent muscle activation patterns reflecting the volitional control of human motion [ 7 ], it has been used in multiple studies to identify locomotion activities [ 8 , 9 , 10 , 11 , 12 ]. Huang et al [ 13 ] developed a gait-phase-dependent model using sixteen channels of lower limb sEMG signals to identify seven locomotion modes.…”

Section: Introductionmentioning

confidence: 99%

The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction

Meng

Pang

Wang

et al. 2021

Sensors

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Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods—support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)—where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.

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