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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
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.
Adolph, K. E., & Robinson, S. R. (2011). Sampling development. Journal of Cognition and Development, 12, 411–423. https://doi.org/10.1080/15248372.2011.608190
Adolph, K. E., & Tamis-LeMonda, C. S. (2014). The costs and benefits of development: The transition from crawling to walking. Child Development Perspectives, 8, 187–192. https://doi.org/10.1111/cdep.12085
Adolph, K. E., Vereijken, B., & Denny, M. A. (1998). Learning to crawl. Child Development, 69, 1299–1312. https://doi.org/10.1111/j.1467-8624.1998.tb06213.x
Adolph, K. E., Robinson, S. R., Young, J. W., & Gill-Alvarez, F. (2008). What is the shape of developmental change? Psychological Review, 115, 527–543. https://doi.org/10.1037/0033-295X.115.3.527
Airaksinen, M., Räsänen, O., Ilén, E., Häyrinen, T., Kivi, A., Marchi, V., et al. (2020). Automatic posture and movement tracking of infants with wearable movement sensors. Scientific Reports, 10(1), 1–13.
Airaksinen, M., Gallen, A., Kivi, A., Vijayakrishnan, P., Häyrinen, T., Ilén, E., …, Vanhatalo, S. (2022). Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants. Communications Medicine, 2(1). https://doi.org/10.1038/s43856-022-00131-6
Arif, M., & Kattan, A. (2015). Physical activities monitoring using wearable acceleration sensors attached to the body. PLoS ONE, 10, e0130851.
Aust, F., Barth, M. (2022). papaja: Prepare reproducible APA journal articles with R Markdown. Retrieved from https://github.com/crsh/papaja. Accessed 10 Feb 2023.
Bergelson, E., Amatuni, A., Dailey, S., Koorathota, S., & Tor, S. (2019). Day by day, hour by hour: Naturalistic language input to infants. Developmental Science, 22, e12715.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Bruijns, B. A., Truelove, S., Johnson, A. M., Gilliland, J., & Tucker, P. (2020). Infants’ and toddlers’ physical activity and sedentary time as measured by accelerometry: A systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity, 17(1), 14.
Casillas, M., Elliott, M. (2021). Cross-cultural differences in children’s object handling at home. PsyArXiv.
Chen, Q., Schneider, J. L., West, K. L., Iverson, J. M. (2022). Infant locomotion shapes proximity to adults during everyday play in the U.S. Infancy. https://doi.org/10.1111/infa.12503
Clerkin, E. M., Hart, E., Rehg, J. M., Yu, C., & Smith, L. B. (2017). Real-world visual statistics and infants’ first-learned object names. Philosophical Transactions of the Royal Society B, 372, 20160055.
Cliff, D. P., Reilly, J. J., & Okely, A. D. (2009). Methodological considerations in using accelerometers to assess habitual physical activity in children aged 0–5 years. Journal of Science and Medicine in Sport, 12(5), 557–567.
Cristia, A., Lavechin, M., Scaff, C., Soderstrom, M., Rowland, C., Räsänen, O., …, Bergelson, E. (2020). A thorough evaluation of the language environment analysis (LENA) system. Behavior Research Methods, 53(2), 467–486. https://doi.org/10.3758/s13428-020-01393-5
Dancho, M., & Vaughan, D. (2023). Timetk: A tool kit for working with time series in R.
de Barbaro, K. (2019). Automated sensing of daily activity: A new lens into development. Developmental Psychobiology, 61(3), 444–464.
de Barbaro, Kaya, & Fausey, C. M. (2022). Ten lessons about infants’ everyday experiences. Current Directions in Psychological Science, 31(1), 28–33. https://doi.org/10.1177/09637214211059536
Franchak, J. M. (2019). Changing opportunities for learning in everyday life: Infant body position over the first year. Infancy, 24, 187–209.
Franchak, J. M. (2020). The ecology of infants’ perceptual-motor exploration. Current Opinion in Psychology, 32, 110–114.
Franchak, J. M., Kretch, K. S., & Adolph, K. E. (2018). See and be seen: Infant-caregiver social looking during locomotor free play. Developmental Science, 21, e12626.
Franchak, J. M., Scott, V., Luo, C. (2021). A contactless method for measuring full-day, naturalistic motor behavior using wearable inertial sensors. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.701343
Galland, B. C., Taylor, B. J., Elder, D. E., & Herbison, P. (2012). Normal sleep patterns in infants and children: A systematic review of observational studies. Sleep Medicine Reviews, 16(3), 213–222. https://doi.org/10.1016/j.smrv.2011.06.001
Greenspan, B., Cunha, A. B., & Lobo, M. A. (2021). Design and validation of a smart garment to measure positioning practices of parents with young infants. Infant Behavior and Development, 62, 101530.
Grolemund, G., & Wickham, H. (2011). Dates and times made easy with lubridate. Journal of Statistical Software, 40(3), 1–25.
Herzberg, O., Fletcher, K. K., Schatz, J. L., Adolph, K. E., & Tamis-LeMonda, C. S. (2021). Infant exuberant object play at home: Immense amounts of time-distributed, variable practice. Child Development, 93(1), 150–164.
Kachergis, G., Yu, C., & Shiffrin, R. M. (2017). A bootstrapping model of frequency and context effects in word learning. Cognitive Science, 41(3), 590–622.
Kadooka, K., Caufield, M., Fausey, C. M., Franchak, J. M. (2021, April). Visuomotor learning opportunities are nested within everyday activities. Paper Presented at the Biennial Meeting of the Society for Research in Child Development.
Karasik, L. B., Tamis-LeMonda, C. S., & Adolph, K. E. (2011). Transition from crawling to walking and infants’ actions with objects and people. Child Development, 82, 1199–1209. https://doi.org/10.1111/j.1467-8624.2011.01595.x
Karasik, L. B., Kuchirko, Y., Dodojonova, R. M., Elison, J. T. (2022). Comparison of U.S. And Tajik infants’ time in containment devices. Infant and Child Development, 31(4). https://doi.org/10.1002/icd.2340
Kretch, K. S., Franchak, J. M., & Adolph, K. E. (2014). Crawling and walking infants see the world differently. Child Development, 85, 1503–1518. https://doi.org/10.1111/cdev.12206
Kwon, S., Zavos, P., Nickele, K., Sugianto, A., & Albert, M. V. (2019). Hip and wrist-worn accelerometer data analysis for toddler activities. International Journal of Environmental Research and Public Health, 16(14), 2598.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159. https://doi.org/10.2307/2529310
Liaw, A., Wiener, M., et al. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
Lobo, M. A., Hall, M. L., Greenspan, B., Rohloff, P., Prosser, L. A., & Smith, B. A. (2019). Wearables for pediatric rehabilitation: How to optimally design and use products to meet the needs of users. Physical Therapy, 99(6), 647–657.
Luo, C., & Franchak, J. M. (2020). Head and body structure infants’ visual experiences during mobile, naturalistic play. PLoS ONE, 15, e0242009.
Majnemer, A., & Barr, R. G. (2005). Influence of supine sleep positioning on early motor milestone acquisition. Developmental Medicine and Child Neurology, 47, 370–376.
Malachowski, L. G., Salo, V. C., Needham, A. W., Humphreys, K. L. (2023). Infant placement and language exposure in daily life. Infant and Child Development.https://doi.org/10.1002/icd.2405
Mendoza, J. K., Fausey, C. M. (2022). Everyday parameters for episode-to-episode dynamics in the daily music of infancy. Cognitive Science, 46(8). https://doi.org/10.1111/cogs.13178
Nam, Y., & Park, J. W. (2013). Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor. IEEE Journal of Biomedical and Health Informatics, 17, 420–426.
Patel, P., Shi, Y., Hajiaghajani, F., Biswas, S., & Lee, M.-H. (2019). A novel two-body sensor system to study spontaneous movements in infants during caregiver physical contact. Infant Behavior and Development, 57, 101383. https://doi.org/10.1016/j.infbeh.2019.101383
Perry, L. K., Prince, E. B., Valtierra, A. M., Rivero-Fernandez, C., Ullery, M. A., Katz, L. F., …, Messinger, D. S. (2018). A year in words: The dynamics and consequences of language experiences in an intervention classroom. PLOS ONE, 13(7), e0199893. https://doi.org/10.1371/journal.pone.0199893
Preece, S. J., Goulermas, J. Y., Kenney, L. P. J., & Howard, D. (2009). A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Transactions on Biomedical Engineering, 56, 871–879.
R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Raz, H. K., Abney, D. H., Crandall, D., Yu, C., & Smith, L. B. (2019). How do infants start learning object names in a sea of clutter? Annual Conference of the Cognitive Science Society, 521–526.
Ren, X., Ding, W., Crouter, S. E., Mu, Y., & Xie, R. (2016). Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning. Applied Intelligence, 45(2), 512–529.
Stewart, T., Narayanan, A., Hedayatrad, L., Neville, J., Mackay, L., & Duncan, S. (2018). A dual-accelerometer system for classifying physical activity in children and adults. Medicine and Science in Sports and Exercise, 50(12), 2595–2602.
Tamis-LeMonda, C. S., Custode, S., Kuchirko, Y., Escobar, K., & Lo, T. (2018). Routine language: Speech directed to infants during home activities. Child Development, 90(6), 2135–2152. https://doi.org/10.1111/cdev.13089
Thurman, S. L., & Corbetta, D. (2017). Spatial exploration and changes in infant-mother dyads around transitions in infant locomotion. Developmental Psychology, 53, 1207–1221.
Warlaumont, A. S., Sobowale, K., & Fausey, C. M. (2021). Daylong mobile audio recordings reveal multitimescale dynamics in infants’ vocal productions and auditory experiences. Current Directions in Psychological Science, 31(1), 12–19. https://doi.org/10.1177/09637214211058166
Wass, S., Phillips, E., Smith, C., Fatimehin, E. O., Goupil, L. (2022). Vocal communication is tied to interpersonal arousal coupling in caregiver-infant dyads. eLife, 11. https://doi.org/10.7554/elife.77399
Weisleder, A., & Fernald, A. (2013). Talking to children matters: Early language experience strengthens processing and builds vocabulary. Psychological Science, 24, 2143–2152.
Yao, X., Plötz, T., Johnson, M., & de Barbaro, K. (2019). Automated detection of infant holding using wearable sensing: Implications for developmental science and intervention. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(2), 1–17.
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.
This work was funded by National Science Foundation Grant BCS #1941449.
The authors have no competing interests to declare that are relevant to the content of this article.
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.
Consent to participate
All caregivers provided written informed consent prior to the start of the study
Consent to publish
Additional written consent was obtained by caregivers for data sharing of audio and video data.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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