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Long-form recording of infant body position in the home using wearable inertial sensors

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Abstract

Long-form audio recordings have had a transformational effect on the study of infant language acquisition by using mobile, unobtrusive devices to gather full-day, real-time data that can be automatically scored. How can we produce similar data in service of measuring infants’ everyday motor behaviors, such as body position? The aim of the current study was to validate long-form recordings of infant position (supine, prone, sitting, upright, held by caregiver) based on machine learning classification of data from inertial sensors worn on infants’ ankles and thighs. Using over 100 h of video recordings synchronized with inertial sensor data from infants in their homes, we demonstrate that body position classifications are sufficiently accurate to measure infant behavior. Moreover, classification remained accurate when predicting behavior later in the session when infants and caregivers were unsupervised and went about their normal activities, showing that the method can handle the challenge of measuring unconstrained, natural activity. Next, we show that the inertial sensing method has convergent validity by replicating age differences in body position found using other methods with full-day data captured from inertial sensors. We end the paper with a discussion of the novel opportunities that long-form motor recordings afford for understanding infant learning and development.

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Data availability

A Databrary repository (https://nyu.databrary.org/volume/1580) includes an exemplar participant’s recording session, with the raw video data files, the Datavyu annotations of those video files, a log file with machine-readable synchronization points and nap/diaper change times, and accelerometer and gyroscope data for each of the four sensors. A GitHub repository (https://doi.org/10.5281/zenodo.8312007) contains the exemplar participant’s data and source code to: (1) synchronize IMU and video annotations, (2) calculate windowed motion features for their data, and (3) train and test the body position classifier using an “individual model”. Because of the overall size of the full dataset and the computational power/time required to synchronize and create windowed datasets for each session, it would not be feasible to reproduce the calculations for all 34 sessions. However, in a second GitHub repository (https://doi.org/10.5281/zenodo.8310338) we share the full results of those computations: The dataset of windowed motion features with corresponding body position codes used to validate the method.

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Author note

We are grateful to Vanessa Scott, Tasnia Haider, and Ishapreet Kaur for their help in collecting data for the present study and to the research assistants of the UCR Perception, Action, and Development Lab for annotating videos.

Funding

This work was funded by National Science Foundation Grant BCS #1941449.

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Authors and Affiliations

  1. Department of Psychology, UC Riverside, 900 University Avenue, Riverside, CA, 92521, USA

    John M. Franchak, Maximilian Tang, Hailey Rousey & Chuan Luo

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  1. John M. Franchak
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  2. Maximilian Tang
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Correspondence to John M. Franchak.

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The authors have no competing interests to declare that are relevant to the content of this article.

Ethics approval

The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki. The study procedures were approved by the Institutional Review Board of the University of California, Riverside, Protocol HS-15-050.

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All caregivers provided written informed consent prior to the start of the study

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Additional written consent was obtained by caregivers for data sharing of audio and video data.

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Franchak, J.M., Tang, M., Rousey, H. et al. Long-form recording of infant body position in the home using wearable inertial sensors. Behav Res (2023). https://doi.org/10.3758/s13428-023-02236-9

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