Related Experiment Video
Updated: Dec 31, 2025
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
Published on: May 17, 2024
Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors
Manu Airaksinen1,2, Okko Räsänen3,4, Elina Ilén5
1Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland. manu.airaksinen@aalto.fi.
Insights
A new smart jumpsuit with sensors accurately tracks infant movements, aiding early detection of neurodevelopmental disorders. This wearable technology achieves human-level accuracy in classifying infant posture and movement patterns.
Area of Science:
- Biomedical Engineering
- Developmental Neuroscience
- Machine Learning in Healthcare
Background:
- Infant movement patterns reflect central nervous system development.
- Early identification of atypical motor development can predict neurodevelopmental disorders.
- Wearable technology offers automated movement assessment.
Purpose of the Study:
- To develop and validate an infant wearable smart jumpsuit for motor activity assessment.
- To train a machine learning algorithm for automatic classification of infant movements.
- To evaluate the accuracy of the system against human observers.
Main Methods:
- Development of a multi-sensor smart jumpsuit for collecting accelerometer and gyroscope data.
- Manual annotation of infant movements and postures using a novel scheme.
- Training a deep convolutional neural network (CNN) for automated classification.
- Comparison of sensor configurations to determine optimal performance.
Main Results:
- The smart jumpsuit system achieved human-equivalent accuracy in classifying infant posture and movement.
- The system demonstrated quantitative tracking of infant movement activities.
- Four-limb sensor configuration yielded the best classification performance.
- Quantified observer ambiguity and proposed its use for classifier improvement.
Conclusions:
- The developed infant wearable technology provides a scalable and automated method for assessing motor development.
- This technology holds potential for early detection of infants at risk for neurodevelopmental disorders.
- The system's accuracy meets human inter-rater agreement levels, validating its utility.
Abstract:
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.
