AI-IoT integration frameworks for stillbirth monitoring and Materiovigilance.
| Framework layer | AI-IoT components | Role in stillbirth monitoring | Role in Materiovigilance & risk reduction | Representative references |
|---|---|---|---|---|
| Sensing layer | Foetal movement, pressure, phonocardiography, ECG/PPG wearables. | Continuous acquisition of foetal activity, foetal heart sounds, and maternal physiological messages. | Early detection of material breakdown, over-contact pressure and skin-device contacts. | [26, 27] |
| Data acquisition & IoT connectivity | BLE, Wi-Fi, IoMT gateways, wearables connected to the cloud. | Provides the transmission of maternal-foetal information in real-time to facilitate round-the-clock monitoring. | The remote device performance tracking, fault detection, and adverse event reporting are supported. | [22] |
| Edge intelligence layer | ML-based artefact removal algorithms, edge AI. | Reduces motion artifacts and improves fetal signal reliability. | Detects abnormal sensor behavior, drift, or hardware malfunction in early stages. | [28] |
| Cloud analytics layer | Time-series analysis, predictive models, and deep learning. | Determines abnormal foetal movements and early foetal distress. | Anticipates failure of devices, false positives, and material fatigue. | [29, 30] |
| Clinical decision support systems (CDSS) | Risk scoring, alerts, dashboards: AI. | Helps clinicians make decisions that can help the foetus and intervene. | Flags suspicious activity of devices and assists in reporting of regulations. | [31] |
| User interface layer | M-health applications, clinician portals, visualisations. | Enhances the comprehension of foetal health indicators to users. | Cuts psychological stress levels by cutting false alarms and enhancing transparency. | [32] |
| Materiovigilance & feedback loop | Adverse event detection under AI control, automated reporting. | Correlates maternal births to device performance statistics. | Facilitates active after-market monitoring, design efficiency, and safety assurance. | [33] |
| Security & privacy layer | AI in cybersecurity and anomaly detection, encrypted IoT protocols. | Secures confidential maternal/foetal health information. | Eliminates manipulation of data, unauthorized access, and safety hazards. | [34] |
AI: artificial intelligence; ECG: electrocardiography; IoT: Internet of Things; ML: machine learning.
The supplementary tables for this article are available at: https://www.explorationpub.com/uploads/Article/file/1001399_sup_1.xlsx.
Authors (Atreyi Pramanik and Pardeep Yadav) are thankful to Ms. Shivani Singh, Ph.D. Scholar of the Uttaranchal University C/o Dr. Pardeep Yadav, for her assistance during the extraction of the gene’s information, presented in the supplementary files. During the preparation of this work, the author(s) used ChatGPT in order to improve the grammar and syntax. After using the tool/service, author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.
AP: Writing—original draft, Formal analysis, Data curation. PY: Conceptualization, Methodology, Writing—review & editing, Supervision. SKJ: Investigation, Visualization, Validation. SPP: Resources, Software, Data curation. PG: Conceptualization, Supervision, Project administration, Writing—review & editing. All authors read and approved the submitted version.
The authors declare that there are no conflicts of interest.
Not applicable.
Not applicable.
Not applicable.
The supplementary datasets and figures can be found in the supplementary files.
Not applicable.
© The Author(s) 2026.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.