AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature Review

Abstract

General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl's assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

Keywords: artificial neural network; cerebral palsy; fidgety movements; general movement assessment; machine learning; motion sensors; multimodal sensing; physical activity assessment; visual sensors.

Figure 1

The procedure of literature selection and screening.

Figure 2

This figure shows necessary steps to solve a classification problem.

Figure 3

This figure shows the tree diagram of the infant’s General Movements Assessment (GMA) methods based on three different categories of sensors. It further categorizes visual sensors-based methods into marker-based and marker-free. It also divides multimodal sensors-based methods into decision and feature fusions.