OpenPose-based model for infant cerebral palsy auxiliary detection

Xinhao Liao; Yu Han; Senlin Cheng

doi:10.1117/12.2684596

1 August 2023 OpenPose-based model for infant cerebral palsy auxiliary detection

Xinhao Liao, Yu Han, Senlin Cheng

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 1275414 (2023) https://doi.org/10.1117/12.2684596
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Aiming at the poor application of diagnosis of infant neuromotor diseases such as cerebral palsy based on computer vison, an OpenPose-based detection model for infant cerebral palsy by extracting features from infant spontaneous motion is proposed. Firstly, the deep separable convolution and the residual network structure is used to reduce the degradation of network operation and improve the detection accuracy of joint points. Then, it gives a loss function model based on Smooth L1 to improve the detection accuracy of infant motion features. Finally, motion characteristics are assigned to support vector machine to classify the infant cerebral palsy, which realizes pre-diagnosis with several features. Experiments conducted on dataset show the accuracy of this proposed method is 7~8% higher than others and reduce the amount of calculation to 1/9 of the original and accuracy of prediction can reach 91.89%. The results show that the detection model is feasible and effective for on-line pre-diagnosis of infant cerebral palsy.

Citation Download Citation

Xinhao Liao, Yu Han, and Senlin Cheng "OpenPose-based model for infant cerebral palsy auxiliary detection", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 1275414 (1 August 2023); https://doi.org/10.1117/12.2684596

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KEYWORDS

Convolution

Motion models

Video

Education and training

Mathematical optimization

Brain diseases

Data modeling

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