Abstract
Since surface electromyograghic (sEMG) signals are non-invasive and capable of reflecting humans’ motion intention, they have been widely used for the motion recognition of upper limbs. However, limited research has been conducted for lower limbs, because the sEMGs of lower limbs are easily affected by body gravity and muscle jitter. In this paper, sEMG signals and accelerometer signals are acquired and fused to recognize the motion patterns of lower limbs. A curve fitting method based on median filtering is proposed to remove accelerometer noise. As for movement onset detection, an sEMG power spectral correlation coefficient method is used to detect the start and end points of active signals. Then, the time-domain features and wavelet coefficients of sEMG signals are extracted, and a dynamic time warping (DTW) distance is used for feature extraction of acceleration signals. At last, five lower limbs’ motions are classified and recognized by using Gaussian kernel-based linear discriminant analysis (LDA) and support vector machine (SVM) respectively. The results prove that the fused feature-based classification outperforms the classification with only sEMG signals or accelerometer signals, and the fused feature can achieve 95% or higher recognition accuracy, demonstrating the validity of the proposed method.
Highlights
According to data released by the China Disabled Persons’ Federation, there were about85.2 million disabled people in China in 2010, among which the physically disabled make up the largest proportion, up to about 29.07% [1]
An electromyograghic (EMG) signal is the result of neuromuscular excitement and the release of bioelectricity during the human body’s autonomic movement; it can be used to identify the state of muscles and perceive movement intention
The features of surface EMG (sEMG) and plantar mechanic signals were analyzed and compared, and the results showed that if the two signal sources were fused, the recognition effect was obviously better than using sEMG or using mechanical signals alone, with a recognition rate of about 95%
Summary
According to data released by the China Disabled Persons’ Federation, there were about. On the other hand, using kinematics signals (such as accelerometer signals) alone can obtain a better recognition effect for different motions of the lower limbs. Juan et al [28] realized the identification and detection of lower limb movements by an integration of acceleration and sEMG signals, and achieved good results. Given that the requirement of an acceleration signal device is simple and portable, and the accelerometer signals contain rich gait information, two sources of signals—sEMG and acceleration—will be used in this paper for lower limb motion recognition. The results show that the fused feature-based classification outperforms the classification with only sEMG signals or accelerometer signals, and the fused feature can achieve 95% or higher recognition accuracy, demonstrating the validity of the proposed method
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