Akademik Veri Yönetim Sistemi
COMPUTER, cilt.59, sa.9, ss.1-6, 2026 (SCI-Expanded, Scopus)
This study presents a digital twin-based system for continuous, remote monitoring and multisource data collection of patients with Amyotrophic Lateral Sclerosis (ALS). The system addresses the limitations of infrequent clinical evaluations by enabling real-time, home-based tracking of disease progression. An Internet of Things (IoT) platform integrated with a camera, a microphone, and a physiological sensor captures data on speech, respiration, and movement. Collected data is securely transmitted to the cloud and mapped to ALS Functional Rating Scale-Revised (ALSFRS-R) domains using an AI Agent. A dashboard visualizes real-time data and long-term trends for clinicians and caregivers. The proposed framework is a candidate to eliminate the inability of ALS patients to reach the clinic or the possible disconnections between the patient and the clinician over time. Additionally, it provides actionable information, supports continuous monitoring of disease progression, early and easy detection, and facilitates timely interventions. It reduces clinical workload and establishes a foundation for future applications in other neurodegenerative disorders.