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Smart Sensors in Everyday Life: Field-Based Assessment of Normal and Pathological Human Behavior

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 978

Editor


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Guest Editor
Centre Borelli, ENS Paris-Saclay, 91190 Gif-sur-Yvette, France
Interests: human movement; wearable; gait

Special Issue Information

Dear Colleagues,

For both disciplinary and ideological reasons, human behavior—whether considered “normal” or pathological—has long been only sparsely quantified, and when it has been, this has most often occurred within the controlled setting of the laboratory. Since the early 2000s, however, the rise of the Internet of Things (IoT) and artificial intelligence (AI) has fundamentally changed the landscape: it is now possible to collect rich, continuous, real‑world data on activities, postures, movements and interactions, to rapidly build large raw and curated datasets, and to analyze them using a wide range of computational tools, with reasonable costs and without relying exclusively on medical staff.

This shift from the lab to the field goes hand in hand with a major paradigm change. Beyond homogeneous cohorts and averaged behavioral profiles, it has become feasible to follow individuals longitudinally, at high temporal resolution, in their everyday environments. Such an approach opens up major perspectives for precision medicine and prevention, for the design of genuinely user‑centered human–machine interfaces, for fine‑grained monitoring of athletic performance, and for the analysis of artistic practice and creative processes.

The aim of this special issue is to capture this pivotal moment by bringing together contributions that explore the in‑the‑wild quantification of human behavior—normal or pathological—across a broad range of domains (clinical settings, sports, human–machine interaction, artistic environments, workplaces, public spaces, and beyond). We welcome methodological and empirical work, experimental and observational designs alike, as well as papers focusing on sensor development, metrics, modeling, or concrete applications. Contributions are also expected to engage explicitly with the ethical challenges raised by these approaches, including data protection, privacy, algorithmic transparency and explainability, and the fair distribution of benefits.

Dr. Pierre Paul Vidal
Guest Editor

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Keywords

  • wearable sensors
  • health monitoring
  • behavioral sensing
  • human–machine interaction
  • biomechanics
  • movement analysis

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Published Papers (1 paper)

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Research

18 pages, 1172 KB  
Article
Longitudinal Infant Sleep Monitoring Using a Sensor-Enabled Responsive Bassinet: A Population-Scale Feasibility Study
by Savannah Gluck, Teresa A. Lillis, Karthik Aroor, Christopher M. Laine and Harvey Karp
Sensors 2026, 26(13), 3990; https://doi.org/10.3390/s26133990 - 24 Jun 2026
Viewed by 691
Abstract
Sleep is crucial to infant development, and excessive sleep disturbances are associated with adverse outcomes for both infants and their caregivers. There is limited information on the longitudinal development of sleep (e.g., duration, fragmentation, etc.) from birth to 6 months of age. New [...] Read more.
Sleep is crucial to infant development, and excessive sleep disturbances are associated with adverse outcomes for both infants and their caregivers. There is limited information on the longitudinal development of sleep (e.g., duration, fragmentation, etc.) from birth to 6 months of age. New technologies, which include real-time environmental sensing and responses, have the potential to overcome many of the traditional limitations on infant sleep monitoring. In this study, we demonstrate the feasibility of utilizing aggregated activity logs from a commercially available IoT (Internet of Things) bassinet to derive traditional sleep metrics (longest sleep stretch, total night sleep, and sleep efficiency), as well as novel metrics related to infant fussing and impacts of the bed’s ability to deliver responsive motion and sound. A total of 26,187 infants (1000–8000 per night) were included in this analysis. A data-driven approach was utilized to define the temporal boundaries of each night, divide each night into periods of sleep and fussing, and identify appropriate nights for inclusion. The derived data provide, in unprecedented resolution, a detailed longitudinal view of infant sleep in this specific population. Our results generally align with previous studies of traditional sleep metrics; however, they also demonstrate a methodological framework for descriptive or comparative monitoring of sleep and soothing, and uniquely characterize dyadic interactions that are not well-captured by traditional metrics. For example, the bassinet’s activity logs indicate not only the proportion of fussing episodes that are resolved without caregiver intervention (e.g., removal), but also reflect the delay between fussing and the need for caregiver intervention. Further evaluation of this sensor-enabled, responsive technology in relation to sleep and fussing is merited. Full article
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