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Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

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Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition.

Kyungkoo Jun¹, Soonpil Choi²

¹Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea.

Sensors (Basel, Switzerland)

|November 17, 2020

Summary

This study developed a deep learning model for infant activity recognition using accelerometer data. The model accurately classifies infant movements, aiding in safety and wellness monitoring for non-verbal newborns.

Keywords:

deep learning human activity recognition image processing newborn unsupervised

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Area of Science:

Biomedical Engineering
Machine Learning
Infant Care Technology

Background:

Existing human activity recognition (HAR) research primarily targets adults, overlooking the unique needs of infants.
Infants' inability to communicate verbally makes HAR crucial for their safety and well-being.
Infant activities differ significantly from adult activities in type and intensity, necessitating specialized study.

Purpose of the Study:

To develop a novel method for recognizing newborn and infant activities using sensor data.
To classify four distinct infant activities: sleeping, distressed movement, normal movement, and externally induced movement.
To address the gap in HAR research concerning the specific behaviors of infants.

Main Methods:

Collected 11 hours of video and synchronized accelerometer data from 10 infant subjects.
Proposed an end-to-end deep learning model integrating an autoencoder and k-means clustering.
Employed an unsupervised learning approach for model training.

Main Results:

The proposed model achieved a balanced accuracy of 0.96.
The model demonstrated a strong performance with an F-1 score of 0.95.
The system effectively distinguished between various infant activities based on sensor data.

Conclusions:

The developed deep learning model shows high efficacy in infant activity recognition.
This technology can significantly contribute to infant safety and health monitoring systems.
Unsupervised learning with autoencoders and k-means clustering is a viable approach for infant behavior analysis.