NCBI PubMed Central
open access
Machine Learning-Based Prediction Model of Pilgrims’ Tiredness During Hajj Using Smartwatch Physiological and Mobility Indicators.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach#372148
- Development and Internal Validation of an Explainable Machine Learning Model for Physical Activity Adherence Among Community-Dwelling Patients with Type 2 Diabetes Mellitus.#823508
- A Tripartite Framework of Conflict-Induced Stress, Acoustic Hypervigilance, and Ramadan Observance as Convergent Determinants of Circadian Decoherence in Athletes.#446194
- Validation of Machine Learning Based Personalized Nutrition Algorithm to Reduce Postprandial Glycemic Excursions Among North American Individuals With Newly Diagnosed Type 2 Diabetes#226220
- Early Detection of noctUrnal hypoglyCemiA in TypE 2 Diabetes Using Pulse Rate Variability Measured by a Smartwatch(EDUCATED-Smartwatch)#804119
Record · ID 796663
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive