Hybrid lightweight Deep-learning model for Sensor-fusion basketball Shooting-posture recognition

Abstract

Shooting-posture recognition is an important area in basketball technical movement recognition domain. This paper proposes the squeeze convolutional gated attention (SCGA) deep-learning model to try identifying various sensor-fusion basketball shooting postures. The model is based on the lightweight SqueezeNet deep-learning model for spatial feature extraction, the gated recurrent unit for time-series feature extraction, and an attention mechanism for feature-weighting calculation. The SCGA model is used to train and test the 10 types of sensor-fusion basketball shooting-posture datasets, and the intra-test achieved an average precision rate of 98.79%, an average recall rate of 98.85%, and a Kappa value of 0.9868. The inter-test achieved a 94.06% average precision rate, 94.57% average recall rate, and a 0.9389 Kappa value. The effectiveness of the SCGA deep-learning model illustrates the potential of the proposed model in recognizing various sensor-fusion basketball shooting postures. This study provides a reference for the field of sports technical-movement recognition.