Gorde:
| Egile nagusia: | |
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| Formatua: | Recurso digital |
| Hizkuntza: | |
| Argitaratua: |
Zenodo
2026
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| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.5281/zenodo.20012358 |
| Etiketak: |
Etiketa erantsi
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Aurkibidea:
- <p>This report presents the design of a Smart Home Fetal Monitoring System intended as a lowcost engineering prototype for structured at-home observation during pregnancy. The proposed<br>system combines an abdominal wearable belt, IMU and pressure or piezoelectric sensors, an<br>ESP32 controller, BLE or Wi-Fi communication, signal processing, baseline comparison, and<br>a mobile application for session control, visualization, history tracking, alerts, and PDF report<br>export. The project addresses the gap between periodic clinical checks and subjective manual<br>kick counting by proposing a repeatable monitoring workflow that can record movementrelated indicators and summarize them in a doctor-readable form. The prototype is not presented<br>as a diagnostic medical device and does not replace CTG, ultrasound, Doppler examination,<br>emergency care, or medical interpretation by qualified healthcare professionals. Its role is<br>limited to data collection, signal quality assessment, feature extraction, trend observation, and<br>reporting. The expected engineering results are derived from previous studies in the literature.<br>Recent wearable fetal movement systems reported movement-detection performance around<br>90% sensitivity, 87.46% precision, and 88.56% F1-score in an IoT-enabled IMU-based design,<br>while another wearable system reported an average recognition and correct rate of 89.74%.<br>Other works demonstrated the feasibility of acoustic, flexible, and multi-sensor pregnancy<br>monitoring platforms, but also confirmed persistent limitations related to motion artifacts, small<br>datasets, sensor placement repeatability, false alarms, battery life, and clinical validation. Based<br>on these findings, the proposed system is designed as a modular, offline-first, privacy-aware<br>prototype that prioritizes robust sensing, quality warnings, personal baseline comparison, and<br>cautious alert wording rather than direct medical diagnosis.</p> <p><em><span>This work was conducted at Arab International University (AIU), Syria. The official website of the university is: </span></em><span><a href="https://www.aiu.edu.sy"><em><span>https://www.aiu.edu.sy</span></em></a></span></p>