Knee motion pattern classification from trunk muscle based on sEMG signals.

A prominent change is being carried out in the fields of rehabilitation and assistive exoskeletons in order to actively aid or restore legged locomotion for individuals suffering from muscular impairments, muscle weakness, neurologic injury, or disabilities that affect the lower limbs. This paper presents a characterization of knee motion patterns from Surface Electromyography (sEMG) signals, measured in the Erector spinae (ES) muscle. Feature extraction (mean absolute value, waveform length and auto-regressive model) and pattern classification methods (Linear Discrimination Analysis, K-Nearest Neighborhood and Support Vector Machine) are applied for recognition of eight-movement classes. Additionally, several channels setup are analyzed to obtain a suitable electrodes array. The results were evaluated based on signals measured from lower limb using quantitative metric such as error rate, sensitivity, specificity and predictive positive value. A high accuracy (> 95%) was obtained, which suggest that it is possible to detect the knee motion intention from ES muscle, as well as to reduce the electrode number (from 2 to 3 channels) to obtain an optimal electrodes array. This implementation can be applied for myoelectric control of lower limb active exoskeletons.