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. 2023 May 16;23(10):4800.
doi: 10.3390/s23104800.

The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development

Alexander Turner  1 , Stephen Hayes  2 , Don Sharkey  3
Affiliations

The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development

Alexander Turner et al. Sensors (Basel). .

Abstract

Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient's home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children's dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children's complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring.

Keywords: autonomous monitoring; deep learning; infant development; movement assessment of infants; neurological development.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An illustration of how the camera is positioned relative to the child.
Figure 2
Figure 2
Two images of the data produced using the 2D marker-less pose estimation algorithm in media pipe. (a) shows all possible markers, and (b) shows a limited set being shown as not all points were visible to the camera.
Figure 3
Figure 3
The results of the BiLSTM network when applied to the classification of infants dexterous movement. (a) The results from the 3 label experiment. (b) The results from the 4 label experiment. In bold are the highest values per column.
Figure 4
Figure 4
The results of the LSTM network when applied to the classification of infants dexterous movement. (a) The results from the 3 label experiment. (b) The results from the 4 label experiment.
Figure 5
Figure 5
The results of the Bi-LSTMCNN network when applied to the classification of infants dexterous movement. (a) The results from the 3 label experiment. (b) The results from the 4 label experiment.
Figure 6
Figure 6
The results of the convolutional neural network (CNN) when applied to the classification of infants dexterous movement. (a) The results from the 3 label experiment. (b) The results from the 4 label experiment.
Figure 7
Figure 7
Confusion matrix presenting the results of the Bi-LSTM-CNN on classification of infant position data.
See this image and copyright information in PMC

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