Gait cadence detection based on surface electromyography (sEMG) of lower limb muscles

Surface electromyography (sEMG) signals could represent the contractions of muscle activity which contains approximately accurate information about joint movements and motor torque. In this study, a new gait cadence detection method was presented by using the sEMG signals of lower limb muscles. To d...

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Bibliographic Details
Published in2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI) pp. 1 - 4
Main Authors Qinglei Sun, Zongtan Zhou, Jun Jiang, Dewen Hu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2014
Subjects
Educational institutions
Electrodes
Electromyography
exoskeleton systems
Exoskeletons
Gait cadence
Legged locomotion
lower limb
Muscles
Surface treatment
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Summary:Surface electromyography (sEMG) signals could represent the contractions of muscle activity which contains approximately accurate information about joint movements and motor torque. In this study, a new gait cadence detection method was presented by using the sEMG signals of lower limb muscles. To detect the gait cadence, sEMG signals were collected from Tibialis Anterior (TA) and Gastronomies Lateral (GL) of lower limb muscles when subjects were walking at different gait cadences. By evaluating the different rhythms of the sEMG activities, different gait cadences of the subject could be detected. In our experiment, subjects were asked to walk at three different cadences, and the averaged estimated error from sEMG signals was 6.8%. Additionally, the different activities of TA and GL muscles in single gait cycle could also be found from the sEMG signals, which could provide an advanced method for human gait analysis. These results showed that the sEMG signals of lower limb muscles varied in a predictable way during walking tasks, which could be detected as input commands to control humanoid robot, exoskeleton assistive systems or other human-machine systems.
DOI:10.1109/MFI.2014.6997665