Sports Motion Recognition based on Foot Trajectory State Sequence Mapping

Huang，Lingjia1; Ma，Hao1; Yan，Weichao1; Liu，Wuda2; Liu，Haoyang3; Yang，Zaiyue1

10.1109/IJCNN.2019.8851921

2019-07-01

2161-4393

978-1-7281-1986-1

Proceedings of the International Joint Conference on Neural Networks

2019-July

1-8

14-19 July 2019

Budapest, Hungary

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

Quantitative motion analysis to evaluate the performance of athletes has been actively studied recently. Although various methods based on wearable inertial sensors have been developed for simple and repetitive movements recognition, the understanding of continuous complex movements of in-field sports is still challenging. In this paper, we propose a new motion segmentation and recognition method based on foot swing trajectory state to achieve robust and efficient recognition of motion of interest (MOI) in the lower limbs from continuous and complex movements. In order to segment complex movements in the lower limbs, a series of foot motion states are defined based on foot-ground contact status and foot trajectory during swing. The lower body motion state sequence combining the states of both feet is matched to a prior knowledge of MOI cycle sequences obtained in advance, so as to obtain a motion type candidate set. In this case, the continuous movement is segmented based on the prescreened motion types to realize adaptive time window for feature extraction. Finally, according to the prescreened motion type candidate set, corresponding trained neural network binary classifiers are used to make the classification based on the calculated kinematic features. The proposed method is verified through experiments of football movements consisting of walking, dribbling and stepover. As the result, the motion type recognition accuracy is 95%.

feature extraction inertial motion capture motion recognition motion segmentation sports analysis.

第一

英语

EI ; CPCI-S

WOS:000530893801113

20194207548525

Complex networks ; Extraction ; Feature extraction ; Joints (anatomy) ; Sports ; Trajectories

Biomechanics, Bionics and Biomimetics:461.3 ; Computer Systems and Equipment:722 ; Chemical Operations:802.3

2-s2.0-85073194577

Scopus

1.Department of Mechanical and Energy EngineeringSouthern University of Science and Technology,China
2.Noitom Ltd.,China
3.Beijing Sport University,China

Huang，Lingjia,Ma，Hao,Yan，Weichao,et al. Sports Motion Recognition based on Foot Trajectory State Sequence Mapping[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:1-8.