Wearable sensors-based assistive technologies for patient health monitoring
Overall, integrating a machine learning model with bio-signals provides an enhanced ability to interpret complex real-time patient health monitoring for personalized care and overall smart healthcare.
Authors
- Danyal Z. Khan (ORCID: https://orcid.org/0000-0001-9213-2550)
- Nouf Abdullah Almujally (ORCID: https://orcid.org/0000-0003-4405-9912)
- Haifa F. Alhasson (ORCID: https://orcid.org/0000-0002-6503-2826)
- Hui Liu (ORCID: https://orcid.org/0000-0002-4265-7241)
- Mohammed Alonazi (ORCID: https://orcid.org/0000-0003-1862-7250)
- Naif Al Mudawi
- Ahmad Jalal (ORCID: https://orcid.org/0009-0000-8421-8477)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Prince Sattam Bin Abdulaziz University (SA)
- Qassim University (SA)
- National University of Modern Languages (PK)
- University of Bremen (DE)
- Korea University (KR)
- Nanjing University of Science and Technology (CN)
- Buraydah Colleges (SA)
- Guodian Institute of Science and Technology (CN)
- Najran University (SA)
- Air University (PK)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-06-02
- DOI
- https://doi.org/10.3389/fbioe.2025.1437877
- Citations
- 13
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 16.59
Funders
- Deutsche Forschungsgemeinschaft
- Princess Nourah Bint Abdulrahman University
- Najran University
- Universität Bremen