Abstract
With the development of microelectronic technology and computer systems, the research of motion intention recognition based on multimodal sensors has attracted the attention of the academic community. Deep learning and other nonlinear neural network models have a wide range of applications in big data sets. We propose a motion intention recognition algorithm based on multimodal long-term and short-term spatiotemporal feature fusion. We divide the target data into multiple segments and use a three-dimensional convolutional neural network to extract the short-term spatiotemporal features. The three types of features of the same segment are fused together and input into the LSTM network for time-series modeling to further fuse the features to obtain multimodal long-term spatiotemporal features with higher discrimination. According to the lower limb movement pattern recognition model, the minimum number of muscles and EMG signal characteristics required to accurately recognize the movement state of the lower limbs are determined. This minimizes the redundant calculation cost of the model and ensures the real-time output of the system results.
Highlights
Deep learning is a kind of simulation of brain behavior, which has a wide range of applications in big data. e two can be connected through a framework or a system
In view of the difficulty of manually designing distinguishable hand shape features in traditional methods, we use 3D-Convolutional neural network (CNN) to extract short-term spatiotemporal features in segments. e input is the motion intent composed of the entire image, avoiding target detection and segmentation
In order to make full use of the features of the three modalities, we adopt the idea of multimodal fusion, and input the three types of features into the Long Short-Term Memory (LSTM) network for time-series modeling, so as to further integrate the features to form a higher-level long-term spatiotemporal feature representation of the target sample
Summary
Deep learning is a kind of simulation of brain behavior, which has a wide range of applications in big data. e two can be connected through a framework or a system. A series of experiments were carried out based on the lower limb data set, and it was determined that, as the number of sampled muscles increases, the average accuracy of intent recognition will increase, but there will be varying degrees of muscle redundancy for specific muscle combinations. Ird, based on the Fisher score, the best feature combination of these muscles was determined, and it was verified in the lower limb data set that the minimal feature subset proposed in this paper can still maintain the original recognition accuracy level so that the muscle and feature selection can achieve the lowest level of redundancy.
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