Objective: Early diagnosis of infant cerebral palsy (CP) is very important for infant health. In this paper, we present a novel training-free method to quantify infant spontaneous movements for predicting CP.
Methods: Unlike other classification methods, our method turns the assessment into a clustering task. First, the joints of the infant are extracted by the current pose estimation algorithm, and the skeleton sequence is segmented into multiple clips through a sliding window. Then we cluster the clips and quantify infant CP by the number of cluster classes.
Results: The proposed method was tested on two datasets, and achieved state-of-the-arts (SOTAs) on both datasets using the same parameters. What's more, our method is interpretable with visualized results.
Conclusion: The proposed method can quantify abnormal brain development in infants effectively and be used in different datasets without training.
Significance: Limited by small samples, we propose a training-free method for quantifying infant spontaneous movements. Unlike other binary classification methods, our work not only enables continuous quantification of infant brain development, but also provides interpretable conclusions by visualizing the results. The proposed spontaneous movement assessment method significantly advances SOTAs in automatically measuring infant health.