Identification of Developmental Delay in Infants Using Wearable Sensors: Full-Day Leg Movement Statistical Feature Analysis
Open accessAbstract
This paper examines how features extracted from full-day data recorded by wearable sensors are able to differentiate between infants with typical development and those with or at risk for developmental delays. Wearable sensors were used to collect full-day (8-13 h) leg movement data from infants with typical development ([Formula: see text]) and infants at risk for developmental delay ([Formula: see text]). At 24 months, at-risk infants were assessed as having good ([Formula: see text]) or poor ([Formula: see text]) developmental outcomes. With this limited size dataset, our statistical analysis indicated that accelerometer features collected earlier in infancy differentiated between at-risk infants with poor and good outcomes at 24 months, as well as infants with typical development. This paper also tested how these features performed on a subset of the data for which the infant movement was known, i.e., 5-min intervals more representative of clinical observations. Our results on this limited dataset indicated that features for full-day data showed more group differences than similar features for the 5-min intervals, supporting the usefulness of full-day movement monitoring.
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Alternative strategies for high-resolution analysis of an infant's motor development involve quantitative data from digital imaging systems or commercial wearable sensors packages, often also with electroencephalography or electromyography.
“Alternative strategies for high-resolution analysis of an infant’s motor development involve quantitative data from digital imaging systems or commercial wearable sensors packages (11–15), often also with electroencephalography (16) or electromyography (17).”
From Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants (2021) · cites this paper as [13]
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Cites
Activity recognition from accelerometer data
Ravi, N., Dandekar, N., Mysore, P., & Littman, M. L. (2005) · Cited by 1,434 · see citing works
Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data
Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015) · JAMA · doi:10.1001/jama.2014.17841 · Cited by 694 · see citing works
Motor Assessment of the Developing Infant
Hurran, C. (1995) · Physiotherapy · doi:10.1016/s0031-9406(05)67120-9 · Cited by 540 · see citing works
Darrah, J., Piper, M., & Watt, M. (1998) · Developmental Medicine & Child Neurology · doi:10.1111/j.1469-8749.1998.tb15399.x · Cited by 314 · see citing works
Activity Recognition from Accelerometer Data on a Mobile Phone
Brezmes, T., Gorricho, J., & Cotrina, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02481-8_120 · Cited by 299 · see citing works
Variation and Variability: Key Words in Human Motor Development
Hadders‐Algra, M. (2010) · Physical Therapy · doi:10.2522/ptj.20100006 · Cited by 256 · see citing works
Prechtl, H. F. R. (1997) · Early Human Development · doi:10.1016/s0378-3782(97)00088-1 · Cited by 246 · see citing works
Johansson, D., Malmgren, K., & Alt Murphy, M. (2018) · Journal of Neurology · doi:10.1007/s00415-018-8786-y · Cited by 188 · see citing works
Wearable technology for cardiology: An update and framework for the future
Pevnick, J. M., Birkeland, K. T., Zimmer, R. J., Elad, Y., & Kedan, I. (2017) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2017.08.003 · Cited by 157 · see citing works
Marcroft, C., Khan, A., Embleton, N. D., Trenell, M., & Plötz, T. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2014.00284 · Cited by 146 · see citing works
Sleep devices: wearables and nearables, informational and interventional, consumer and clinical
Bianchi, M. T. (2017) · Metabolism · doi:10.1016/j.metabol.2017.10.008 · Cited by 124 · see citing works
Meinecke, L., Breitbach‐Faller, N., Bartz, C., Damen, R., Rau, G., & Dißelhorst-Klug, C. (2006) · Human Movement Science · doi:10.1016/j.humov.2005.09.012 · Cited by 122 · see citing works
Heinze, F., Hesels, K., Breitbach‐Faller, N., Schmitz‐Rode, T., & Dißelhorst-Klug, C. (2010) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-010-0624-z · Cited by 92 · see citing works
Daily Quantity of Infant Leg Movement: Wearable Sensor Algorithm and Relationship to Walking Onset
Smith, B. A., Trujillo-Priego, I. A., Lane, C. J., et al. (2015) · Sensors · doi:10.3390/s150819006 · Cited by 81 · see citing works
Self-citationOhgi, S., Morita, S., Loo, K. K., & Mizuike, C. (2008) · Physical Therapy · doi:10.2522/ptj.20070171 · Cited by 57 · see citing works
Assessment of Infant Movement With a Compact Wireless Accelerometer System
Gravem, D., Singh, M., Chen, C., Rich, J., Vaughan, J., Goldberg, K. Y., Waffarn, F., Chou, P. H., Cooper, D. M., Reinkensmeyer, D. J., et al. (2012) · Journal of Medical Devices · doi:10.1115/1.4006129 · Cited by 56 · see citing works
Smith, B. A., Vanderbilt, D. L., Applequist, B. C., et al. (2017) · Technologies · doi:10.3390/technologies5030055 · Cited by 40 · see citing works
Self-citationAdolph, K. E., & Robinson, S. R. (2011) · Journal of Cognition and Development · doi:10.1080/15248372.2011.608190 · Cited by 36 · see citing works
Kinematic characteristics of infant leg movements produced across a full day
Trujillo-Priego, I. A., & Smith, B. A. (2017) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668317717461 · Cited by 33 · see citing works
Self-citationFan, M., Gravem, D., Cooper, D. M., & Patterson, D. J. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370278 · Cited by 29 · see citing works
Activity Recognition Using Wrist-Worn Sensors for Human Performance Evaluation
Nguyen, M., Fan, L., & Shahabi, C. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.199 · Cited by 24 · see citing works
Self-citationLang, M. (2018) · Chapman University Digital Commons (Chapman University) · Cited by 21 · see citing works
Heinze, F., Breitbach-Faller, N., Schmitz‐Rode, T., et al. (2009) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-03889-1_7 · Cited by 1 · see citing works
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Wilson, R. B., Vangala, S., Elashoff, D., Safari, T., & Smith, B. A. (2021) · Sensors · doi:10.3390/s21020616 · Cited by 59 · see citing works
Self-citationJeong, H., Kwak, S. S., Sohn, S., Lee, J. Y., Lee, Y. J., O’Brien, M. K., Park, Y., Avila, R., Kim, J.-T., Yoo, J.-Y., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2104925118 · Cited by 59 · see citing works
- Authors
- Abrishami, M. S., Nocera, L., Mert, M., Trujillo-Priego, I. A., Purushotham, S., Shahabi, C., & Smith, B. A.
- Affiliations
- University of Southern California (US)
- University of Maryland, Baltimore (US)
- Corresponding author
- Abrishami, Mohammad Saeed
- ORCID
- Venue
- IEEE Journal of Translational Engineering in Health and Medicine
- Published
- 2019
- Volume
- 7
- Pages
- 1-7
- ISSN
- 2168-2372 (ISSN-L)
- PMC
- PMC6375381
- OpenAlex
- W2914134360
- Cited by
- 46
- References
- 23
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 13 · 2023: 5 · 2022: 2 · 2021: 4 · 2020: 10 · 2019: 4
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Journal of Translational Engineering in Health and Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- American Physical Therapy Association
- USC Integrated Media Systems Center
- Foundation for Physical Therapy
- Foundation for the National Institutes of Health (K12-HD055929 (KO))
- National Science Foundation (1706964 (BAS and MM))
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Developmental and Educational Psychology › Children's Physical and Motor Development
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Infant Development and Preterm Care
- Keywords
- developmental delay, infants, wearable sensors, accelerometer, leg movement, statistical features, high-risk infants, developmental outcomes
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