Objective To test the hypothesis that backward crawling and forward crawling share similar inter-joint coordination patterns,thus providing potential evidence for the application of backward crawling in rehabilitation training.Methods The acceleration signals in the X,Y,and Z directions for 9 joints(including bilateral wrists,elbows,shoulders,knees,and hips)in 9 volunteers during forward and backward crawling were collected using a custom signal acquisition system,and the pressure signals were also recorded when the palms contacted the ground.The collected acceleration signals were preprocessed,segmented into cycles,and vectorized.Based on the pressure signals,a single crawling cycle was divided into support phase and swing phase.In addition,principal component analysis was applied to extract inter-joint coordination in limbs at various scales(sagittal,coronal,and transverse planes).Pearson correlation coefficients of inter-joint coordination patterns were compared between forward and backward crawling in support period,swing period,and full cycle.Results The correlation coefficients for coordination patterns in the full cycle at the transverse plane scale were 0.813 5(PC1)and 0.837 5(PC2),and the correlation coefficient of the support period PC2 was 0.901 8.At the sagittal plane scale,the correlation coefficient of the support period PC1 was 0.948 5.Conclusion The study provides preliminary evidence that limb motion coordination patterns during backward crawling are similar to those observed during forward crawling.Future research will further explore the effects of backward crawling on functional rehabilitation in individuals with motor impairments.

How to effectively reduce the size of infant pose estimation network models is a key issue restricting the"home-use"of infant pose estimation technology. Therefore,a lightweight method for infant pose estimation in home scenarios is proposed. The method takes the lightweight network MobileNetV3 as the encoding backbone and utilizesa PixelShuffle up-sampling module in the decoder for reducing the quantity of model parameters. Meanwhile,coordinate attention mechanism is used to better capture location information and channel feature information,highlighting the feature information of small targets and occluded human keypoints. Besides,the parallel cross-correlation convolution is further modified to enhance the capability of feature information extraction. The method's performance is verified on the general pose estimation dataset (COCO) and the dedicated infant pose estimation dataset (SyRIP). The results show that,with a calculation volume (GFLOPs) of only 0.96,the method achieves average accuracies of 73.5％ and 91.0％ on COCO and SyRIP datasets,respectively,proving that it can significantly reduce the quantity of model parameters and calculation volume without sacrificing pose estimation accuracy. The proposed lightweight estimation model is expected to be deployed on home appliances such as smart terminals,thereby realizing intelligent estimation of abnormal infant poses in home scenarios.

Objective To test the hypothesis that backward crawling and forward crawling share similar inter-joint coordination patterns,thus providing potential evidence for the application of backward crawling in rehabilitation training.Methods The acceleration signals in the X,Y,and Z directions for 9 joints(including bilateral wrists,elbows,shoulders,knees,and hips)in 9 volunteers during forward and backward crawling were collected using a custom signal acquisition system,and the pressure signals were also recorded when the palms contacted the ground.The collected acceleration signals were preprocessed,segmented into cycles,and vectorized.Based on the pressure signals,a single crawling cycle was divided into support phase and swing phase.In addition,principal component analysis was applied to extract inter-joint coordination in limbs at various scales(sagittal,coronal,and transverse planes).Pearson correlation coefficients of inter-joint coordination patterns were compared between forward and backward crawling in support period,swing period,and full cycle.Results The correlation coefficients for coordination patterns in the full cycle at the transverse plane scale were 0.813 5(PC1)and 0.837 5(PC2),and the correlation coefficient of the support period PC2 was 0.901 8.At the sagittal plane scale,the correlation coefficient of the support period PC1 was 0.948 5.Conclusion The study provides preliminary evidence that limb motion coordination patterns during backward crawling are similar to those observed during forward crawling.Future research will further explore the effects of backward crawling on functional rehabilitation in individuals with motor impairments.

How to effectively reduce the size of infant pose estimation network models is a key issue restricting the"home-use"of infant pose estimation technology. Therefore,a lightweight method for infant pose estimation in home scenarios is proposed. The method takes the lightweight network MobileNetV3 as the encoding backbone and utilizesa PixelShuffle up-sampling module in the decoder for reducing the quantity of model parameters. Meanwhile,coordinate attention mechanism is used to better capture location information and channel feature information,highlighting the feature information of small targets and occluded human keypoints. Besides,the parallel cross-correlation convolution is further modified to enhance the capability of feature information extraction. The method's performance is verified on the general pose estimation dataset (COCO) and the dedicated infant pose estimation dataset (SyRIP). The results show that,with a calculation volume (GFLOPs) of only 0.96,the method achieves average accuracies of 73.5％ and 91.0％ on COCO and SyRIP datasets,respectively,proving that it can significantly reduce the quantity of model parameters and calculation volume without sacrificing pose estimation accuracy. The proposed lightweight estimation model is expected to be deployed on home appliances such as smart terminals,thereby realizing intelligent estimation of abnormal infant poses in home scenarios.

Crawling is a significant sign of gross motor development in infants,and also an important means of rehabilitation training for patients with motor disorders.The accurate measurement of the motion state during human crawling is essential for evaluating the gross motor developmental process in infants and the rehabilitation outcome of patients with motor disorders.In recent years,many studies have attempted to quantitatively evaluate the motion state by detecting physiological signals during human crawling,but there is a lack of overview on human crawling motion signal acquisition and processing.Herein the detection and processing methods for motion signals during human crawling and relevant researches are reviewed.The mainstream methods for detecting motion signals during human crawling are introduced from the perspectives of inertial sensors,pressure sensors,and surface electromyography.Then,the signal processing and analysis such as periodic segmentation,kinematic analysis,and dynamic analysis in human crawling are summarized.Based on the theory of motion coordination,the research advances in joint synergy and muscle synergy during human crawling are elaborated.Finally,the current problems and future development directions of motion analysis for human crawling are discussed.