{ "abstract": "Wearable sensors may enable objective monitoring of infant motor activity, posture, and movement in laboratory, clinic, and home contexts. However, evidence for the validity, generalizability, and implementation readiness of wearable-derived infant motor measures remains heterogeneous. This scoping review synthesized technical features, validation evidence, and clinical applications of wearable sensors monitoring infant motor activity, posture, or movement during awake periods in the first year of life. PRISMA-ScR guidelines were followed, and the protocol was prospectively registered on Open Science Framework. Searches for peer-reviewed articles on wearable-based monitoring of infant activity published between 2019 and 2024 were identified through searches of PubMed, CINAHL, IEEE Xplore, and the ACM Digital Library. A total of 41 studies were selected and classified using Keadle's five-phase validation framework, distinguishing technical validation (Phases I–III) from applied implementation (Phase IV). Fifteen studies (36.6%) included typically developing infants, 22 (53.7%) included infants with or at-risk for developmental disabilities, and 4 (9.8%) were unspecified. Fifteen studies (36.6%) focused on technical validation; 26 (63.4%) implemented sensors in applied contexts. Triaxial accelerometers (n = 15) and IMUs (n = 24) were most common, with sampling frequencies from 1–150 Hz. Posture classification accuracy was 95.8%–98.2% in studies directly comparing posture and movement, whereas fine-grained movement classification was near 81%–84%. Reference-standard correspondence was generally strong for accumulated posture duration (r = 0.80–0.99) but more variable for movement and clinical measures (r = 0.61–0.98). Application studies quantified physical activity, movement coordination and complexity, and intervention responsiveness, with over half collecting home-based data. Wearable sensors have evolved from proof-of-concept systems to family-centered digital health tools supporting developmental monitoring and physical activity promotion. When paired with caregiver feedback and infant-centered design, these technologies may support families, complement clinic-based assessment, and extend developmental support access. Future research should prioritize standardized reporting, affordability, and open-source analytic pipelines to advance equitable implementation. Author summary: Infant movement provides important information about early health and development, but it is difficult to measure continuously during everyday life. Wearable sensors offer a way to monitor infant activity and posture outside the clinic, including in the home. In this review, we examined 41 studies published between 2019 and 2024 that used wearable sensors to measure movement during the first year of life. We found that accelerometers and other motion-sensing devices could often identify infant posture and gross activities with high accuracy. Researchers also used these technologies to study physical activity, movement coordination, developmental differences, and responses to intervention. More than half of the applied studies collected data in home settings, showing growing interest in real-world use. However, differences in devices, methods, reporting, affordability, and data analysis continue to limit comparison and widespread adoption. Wearable sensors may eventually help families and healthcare professionals monitor development over longer periods and complement clinic-based assessments, but more consistent, accessible, and infant-centered approaches are needed.", "abstract_shown": true, "affiliations": [ "Department of Biomedical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts, United States of America", "Division of Physical Therapy, Human Performance, School of Medicine, West Virginia University, Morgantown, West Virginia, United States of America", "Department of Physical Therapy and Kinesiology, Zuckerberg College of Health Sciences, University of Massachusetts, Lowell, Massachusetts, United States of America", "Miner School of Computer and Information Sciences, University of Massachusetts, Lowell, Massachusetts, United States of America", "Solomont School of Nursing, University of Massachusetts, Lowell, Massachusetts, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 6, "authors": [ { "affs": [ 0 ], "name": "Alexa DeVito" }, { "affs": [ 1 ], "name": "Ketaki Inamdar" }, { "affs": [ 2 ], "name": "Margaret E. O'Neil" }, { "affs": [ 3 ], "name": "Yidong Zhu" }, { "affs": [ 4 ], "name": "Mohammad Arif Ul Alam" }, { "name": "Ainat Koren" } ], "bibtex": "https://research.draconitic.com/paper/d0am88m8g3fgf.bib", "cited_by": 0, "doi": "10.1371/journal.pdig.0001809", "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001809", "id": "d0am88m8g3fgf", "issns": [ "2767-3170" ], "landing_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001809", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1707|Context-Aware Activity Recognition Systems" ], [ "plos", "/Biology and life sciences/Anatomy/Body limbs/Legs" ], [ "plos", "/Biology and life sciences/Anatomy/Body limbs/Legs/Ankles" ], [ "plos", "/Biology and life sciences/Biomechanics/Musculoskeletal mechanics" ], [ "plos", "/Biology and life sciences/Physiology/Muscle physiology/Musculoskeletal mechanics" ], [ "plos", "/Engineering and technology/Electronics engineering/Electronics/Accelerometers" ], [ "plos", "/Medicine and health sciences/Anatomy/Body limbs/Legs" ], [ "plos", "/Medicine and health sciences/Anatomy/Body limbs/Legs/Ankles" ], [ "plos", "/Medicine and health sciences/Epidemiology/Medical risk factors" ], [ "plos", "/Medicine and health sciences/Public and occupational health/Physical activity" ], [ "plos", "/People and places/Population groupings/Age groups/Children/Infants" ], [ "plos", "/People and places/Population groupings/Families/Children/Infants" ], [ "plos", "/Physical sciences/Physics/Classical mechanics/Acceleration" ] ], "openalex": "W7225398678", "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001809&type=printable", "pmcid": null, "pmid": null, "published": "2026-11-06", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/d0am88m8g3fgf.ris", "sources": [ { "first_seen": "2026-11-07T12:20:24Z", "native_id": "10.1371/journal.pdig.0001809", "source": "plos", "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001809" } ], "subjects": [ { "field": "medicine", "origin": "plos:anatomy", "subfield": "physiology" } ], "title": "Recent trends of infant motor activity estimation using wearable sensors: A scoping review", "title_text": "Recent trends of infant motor activity estimation using wearable sensors: A scoping review", "type": "peer-reviewed", "updated_at": "2026-11-08T12:20:48Z", "url": "https://research.draconitic.com/paper/d0am88m8g3fgf", "venue": "PLOS Digital Health", "versions": [] }