Human posture recognition based on multiple features and rule learning

Abstract

The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.

Design of Teaching System of Physical Yoga Course in Colleges and Universities Based on Computer Network

“…With the rapid development of newgeneration information technologies such as artificial intelligence, many emerging frontier technologies are widely popularized and applied in daily life. Human body gesture recognition, human motion recognition, and human motion detection technologies have broad application prospects in human motion behavior analysis, medical rehabilitation training, and physical education [24]. In the current situation of the extensive development of university sports MOOC online teaching, in order to design a scientific teaching system, this paper proposes a multimodal yoga gesture detection algorithm, which aims to provide teachers and students in the field of yoga teaching with a more vivid way of interaction.…”

Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods

“…Example images for cases 1-5 are shown in Figures 16-20, respectively. Five cases of scenario [49][50][51][52][53] are described as:…”

Basketball posture recognition based on HOG feature extraction and convolutional neural network

“…Online First It can be seen from figure 6 that the average recognition accuracy of the model is about 0.98 in the end, and the average recognition rate does not improve significantly after that. It can be seen that convolutional neural network has obvious advantages in basketball pose recognition, and the recognition effect is Table 1 lists the recognition results based on random forest [23] and SVM [24]). Especially for attitude recognition results based on improved Gaussian kernel function [25], pose1 and Pose5 are compared, the recognition effects of Gaussian kernel function are 98% and 96%, while the recognition rates of Pose1 and Pose5 described in this paper are about 98.16% and 97.42%, indicating that the convolutional network model has a better performance in attitude recognition.…”

“…Posture recognition of specific users can assist users in learning specific actions. For example, posture recognition of athletes can determine whether a specific posture meets the standard [8,9].…”