Detecting anomalies in smart wearables for hypertension: a deep learning mechanism
The results confirm the potential of integrating ResNet-LSTM into SHM for accurate and non-invasive BP prediction. This approach also highlights the need for accurate anomaly detection in continuous monitoring systems, especially for wearable devices. Future work will focus on enhancing cloud-based infrastructures for real-time analysis and refining anomaly detection models to improve patient outcomes.
Authors
- Fathe Jeribi (ORCID: https://orcid.org/0000-0002-8511-8002)
- Mohammed Alhameed (ORCID: https://orcid.org/0000-0002-3326-936X)
- Mohammed Shuaib (ORCID: https://orcid.org/0000-0001-6657-2587)
- Shadab Alam (ORCID: https://orcid.org/0000-0003-0504-4515)
- C. Kishor Kumar Reddy (ORCID: https://orcid.org/0000-0002-3762-0137)
- R. Madana Mohana (ORCID: https://orcid.org/0000-0002-7652-6266)
- Vijaya Sindhoori Kaza (ORCID: https://orcid.org/0000-0002-1364-8530)
Institutions
- Chaitanya Bharathi Institute of Technology (IN)
- Stanley Medical College (IN)
- Jazan University (SA)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-01-09
- DOI
- https://doi.org/10.3389/fpubh.2024.1426168
- Citations
- 16
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 10.79
Funders
- Jazan University