Orthogonal multilinear discriminant analysis and its subblock tensor analysis version

In order to identify the road slope by leg EMG signal, we use OpenSim to analyze the relationship between ankle movement and calf muscle length; tibialis anterior muscle and gastrocnemius muscle are selected in the EMG signal acquisition test; the EMG signals of chosen muscles are obtained when walking on seven different slope roads; BP neural network is used to identify the signal after time-domain (iEMG, VAR, and RMS) and frequency-domain(MF and MPF) feature analysis. The results show that the EMG signals of tibialis anterior muscle and gastrocnemius muscle can identify the slope of road with an average recognition rate of 87.68%. The results provide a basis for human-computer interaction technology with EMG signal as input.

EMG signal Analysis and Identification of Human Calf Muscles based on Walking on Different Slope Road

To verify whether the EMG signals can recognize different gait under different road conditions, this paper selects 5 kinds of gait (walking on flat ground, uphill, downhill, upstep and downstep), collects the EMG signals of 4 muscles (Biceps femoris, lateral femoral, tibialis anterior, and gastrocnemius) of lower limbs during the movement, and analysis the differences of the peak values of EMG signals under different gait conditions. To compare the recognition effect, the thigh and calf EMG signals were used as input to recognize 5 kinds of gait. The results show that lower limb EMG signals can recognize different gait. The recognition rate for uphill and downhill gait by thigh EMG signal is low (75.37% and 72.20%), and that in calf EMG signal for uphill gait is low (73.94%), but when the 4 muscles of thigh and calf are input together, the recognition rate of the five gait is improved, and the average recognition rate is 95.16%.

Research on Gait Recognition Based on Lower Limb EMG Signal

Gait recognition makes use of human walking patterns for biometric recognition and is a novel topic in the field.Effective segmentation using a simple method to extract silhouettes of walking figures from the background was the first step of our method;this played a key role in gait recognition.Then,morphology was used and a standardized and centralized image was obtained by geometric transformation.Afterwards gait energy image(GEI) was used as a feature extraction method——describing gait characteristics obtained using periodic sequence images according to their cyclical divisions.Following this,feature dimensionality was reduced through principal component analysis(PCA),two-dimensional principal component analysis(2DPCA),complete two-dimensional principal component analysis(C2DPCA) and weighted complete two-dimensional principal component analysis(WC2DPCA) respectively.The nearest neighbor classifier was then used to distinguish different human gaits.By balancing calculation and recognition rates,experimental results demonstrated that 2DPCA based on GEI has encouraging recognition performance with a recognition rate of about 95.43%.

Research on a gait recognition algorithm based on generalized principal component analysis

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Research on a gait recognition algorithm based on generalized principal component analysis