Single-channel SEMG using wavelet deep belief networks for upper limb motion recognition

Publication date: March 2020Source: International Journal of Industrial Ergonomics, Volume 76Author(s): Junkai Shao, Yafeng Niu, Chengqi Xue, Qun Wu, Xiaozhou Zhou, Yi Xie, Xiaoli ZhaoAbstractSurface electromyography (SEMG) has been widely used in different fields such as human machine interaction and motion recognition. A hybrid classification model based on singular value decomposition (SVD) and wavelet deep belief networks (WDBN) is firstly proposed in this paper, which allows the machine to recognize the single-joint motions of upper limb by using one channel. In this experiment, the three-joint SEMG signals of upper limb are respectively recorded through different two channels, which are employed for subsequent comparison to obtain the best single-channel of each joint. Afterwards, the collected raw signals are enhanced by SVD processing. Wavelet function is applied to replace sigmoid function as activation function for feature learning, and the spectrum signals processed by fast Fourier transform (FFT) are input to WDBN model. The results demonstrate that the recognition rates of three joint movements can be up to 100% by SVD-WDBN method, which is much better than support vector machine (SVM), back propagation (BP) neural network and extreme learning machine (ELM) model. The proposed method makes it more possible to control wearable devices with different single-channel SEMG signals, thereby the work efficiency of smart wearable devices can be improved, as well as the c...
Source: International Journal of Industrial Ergonomics - Category: Occupational Health Source Type: research