Study on the Identification Method of Human Upper Limb Flag Movements Based on Inception-ResNet Double Stream Network

Abstract

To investigate the recognition effect of flag motions based on 9 axis sensors, starting with deep learning methods, this paper proposes a framework for feature extraction and recognition of flag movements recognition by employing an improved Inception-ResNet dual-stream network. In traditional signal recognition studies, Support Vector Machine (SVM), Random Forest (RF) and One Dimensional Convolutional Neural Network (CNN) are usually used to extract signals' features. In the meanwhile, the time series data sets such as flag movements are usually the standard data set after processing. Therefore, there are usually some limitations in traditional systems. First, in actual environment, there exists a lack of effective segmentation detection method for the samples of long time series, resulting in the deviation of the data set in the recognition process. Second, the One-Dimensional CNN framework used and the machine learning frameworks used in previous studies are difficult to process large quantities of data with too high computational memory. Based on these problems, this study proposes a signal change point detection model based on the diversity factory function in the signal segmentation and detection stage, miniaturizes the convolution kernel in the original CNN by using the Inception-ResNet(I-R) dual-flow network separable convolution method, and proposes a CrossEntropy-Logistic(C-L) joint classification loss function. Through conducting comparative experiments, it is found that the average calculation parameter of the CNN framework based on the Inception-ResNet model is 2.7×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> , which is approximately 37% lower than the number of 3.7×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> in the original CNN model. Finally, the recognition rate between C-L joint loss function and other models such as Multi-Layer Perceptron and Ensemble Learning in recent years are compared. Compared with Ensemble Learning-CrossEntropy (ELC) model, the C-L joint loss function can improve the recognition rate by nearly 5% according to the results of flag movements identification measured by several classification models.