This study proposed a markerless infant movement assessment system for GM evaluation. This system calculates 25 evaluation indices related to the movements of an infant, such as the movement frequency and rhythm of movement, from binary images extracted from the background difference and frame difference of video images. Movement discrimination based on GMs is also performed using a neural network. The distinctive features of this system are that the movements of infants can be measured without using any markers for motion capture and can discriminate movements based on GMs automatically using a neural network. In the experiments conducted during the study, the evaluation and classification of infant movements based on GMs are demonstrated using the proposed system for full-term and low-birth-weight infants. The results reveal that the proposed system can evaluate infant movements similarly to a licensed evaluator and can classify GMs accurately (average classification rates: 76.2 ± 2.83% for four types of GM classification, 92.9 ± 1.98% for normal/abnormal classification).
Resarch topics
A Clinical Diagnosis Support System for General Movements Evaluation to Assess Spontaneous Movements in Infants
References
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Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), pp. 5614-5617, Milano, Italy, August 25th-29th, 2015.
