Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors

TL;DR

This study compares neural network architectures and data augmentation techniques for infant motility assessment using wearable sensors, highlighting optimal encoder choices, model types, and augmentation strategies to improve robustness and performance.

Contribution

It systematically evaluates different neural network architectures and data augmentation methods, providing practical guidelines for optimizing infant movement classification from wearable sensor data.

Findings

Parallel 2D convolutions yield best sensor encoder performance.

Feed-forward dilated convolutions outperform RNNs in accuracy and training stability.

Data augmentation enhances robustness against sensor dropout and packet loss.

Abstract

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time-series modelling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and non-ideal recording conditions. The experiments are conducted using a data-set of multi-sensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel 2-dimensional convolutions for intra-sensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time-series models revealed that feed-forward dilated convolutions with residual and skip connections outperformed all RNN-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall the results provide tangible suggestions on how to optimize end-to-end neural network training for multi-channel movement sensor data.