# Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors

arXiv: https://arxiv.org/abs/1909.09823 · HTML: https://mlsanity.com/paper/1909.09823/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Unsupervised Human Pose Estimation through Transforming Shape Templates](https://mlsanity.com/paper/2105.04154/) (2021)
- [Automatic infant 2D pose estimation from videos: comparing seven deep neural network methods](https://mlsanity.com/paper/2406.17382/) (2024)
- [Deep learning empowered sensor fusion boosts infant movement classification](https://mlsanity.com/paper/2406.09014/) (2024)
- [Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors](https://mlsanity.com/paper/2107.01086/) (2021)
- [Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies](https://mlsanity.com/paper/2205.10778/) (2022)
- [Infant movement classification through pressure distribution analysis](https://mlsanity.com/paper/2208.00884/) (2022)
- [Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors](https://mlsanity.com/paper/2305.09366/) (2023)
- [PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse](https://mlsanity.com/paper/2411.10087/) (2024)
- [The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations](https://mlsanity.com/paper/2609.04009/) (2026)
- [KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study](https://mlsanity.com/paper/2301.03469/) (2023)

## Abstract (arXiv)

Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group. A machine learning algorithm, based on deep convolutional neural networks (CNNs) was then trained for automatic detection of posture and movement classes using the data and annotations. Our experiments show that the setup can be used for quantitative tracking of infant movement activities with a human equivalent accuracy, i.e., it meets the human inter-rater agreement levels in infant posture and movement classification. We also quantify the ambiguity of human observers in analyzing infant movements, and propose a method for utilizing this uncertainty for performance improvements in training of the automated classifier. Comparison of different sensor configurations also shows that four-limb recording leads to the best performance in posture and movement classification.