Applying Deep Learning-Based Human Motion Recognition System in Sports Competition - Zhang, Liangliang (Frontiers in Neurorobotics, 2022)
The exploration here intends to compensate for the traditional Human Motion Recognition (HMR) systems' poor performance on large-scale datasets and micro motions. To this end, improvement is designed for the HMR in sports competition based on the Deep Learning (DL) algorithm. Firstly, the background and research status of HMR is introduced. Then, a new HMR algorithm is proposed based on Kernel Extreme Learning Machine (KELM)-Multi-dimensional Feature Fusion (MFF). Afterward, a simulation experiment is designed to evaluate the performance of the proposed KELM-MFF-based HMR algorithm. The results show that the recognition rate of the proposed KELM-MFF-based HMR is higher than other algorithms. The recognition rate at ten video frame sampling points is ranked from high to low: the proposed KELM-MFF-based HMR, Support Vector Machine (SVM)-MFF-based HMR, Convolutional Neural Network (CNN) + optical flow [CNN-T]-based HMR, Improved Dense Trajectory (IDT)-based HMR, Converse3D (C3D)-based HMR, and CNN-based HMR. Meanwhile, the feature recognition rate of the proposed KELM-MFF-based HMR for the color dimension is higher than the time dimension, up by 24%. Besides, the proposed KELM-MFF-based HMR algorithm's recognition rate is 92.4% under early feature fusion and 92.1% under late feature fusion, higher than 91.8% and 90.5% of the SVM-MFF-based HMR. Lastly, the proposed KELM-MFF-based HMR algorithm takes the 30s and 15s for training and testing. Therefore, the algorithm designed here can be used to deal with large-scale data sets and capture and recognize micro motions. The research content provides a reference for applying Extreme Learning Machine algorithms in sports competitions.