(untitled) (Digital Health & Telemonitoring Advances E: 3117-6461 | P: 3117-647X, 2026)
The long-term health consequences of COVID-19, commonly referred to as post-COVID syndrome or “Long COVID,” have emerged as a significant global healthcare challenge. Patients recovering from acute infection often experience persistent symptoms, including respiratory dysfunction, cardiovascular complications, neurological impairments, and fatigue. This study explores the role of multi-sensor remote monitoring systems in detecting, tracking, and predicting post-COVID health complications. By integrating wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI), these systems enable continuous and real-time health monitoring outside traditional clinical settings. This paper provides a comprehensive analysis of multi-sensor architectures, data acquisition techniques, and machine learning models used for predictive analytics in post-COVID care. It examines clinical trends observed through sensor data, including variations in heart rate variability, oxygen saturation, respiratory patterns, and physical activity levels. Advanced predictive models, including deep learning and time-series analysis, are evaluated for their ability to identify early signs of deterioration and long-term complications. Despite promising advancements, challenges such as data heterogeneity, privacy concerns, and clinical validation remain significant barriers. This paper discusses these limitations and proposes future research directions, including multimodal data fusion and personalized predictive modeling. The findings highlight the potential of multi-sensor remote monitoring systems to transform post-COVID care by enabling proactive, data-driven, and patient-centric healthcare.