<![CDATA[The rapid expansion of telemedicine and wearable health devices has intensified the demand for energy-efficient and adaptable embedded systems capable of supporting real-time, reliable remote health monitoring. This review provides a comprehensive survey of reconfigurable embedded platforms—focusing on field-programmable gate arrays (FPGAs), coarse-grained reconfigurable arrays (CGRAs), and heterogeneous system-on-chips (SoCs)—deployed for monitoring critical physiological parameters such as electrocardiogram (ECG), oxygen saturation (SpO₂), and body temperature. We analyze co-design methodologies that integrate artificial intelligence (AI-driven) neural accelerators, quantization strategies, and runtime adaptability to address the competing requirements of low power consumption, data integrity, and latency minimization in diverse telemedicine contexts. The paper highlights the strengths and limitations of conventional versus reconfigurable approaches, reviews case studies in wearable and implantable health devices, and underscores key design trade-offs in performance, scalability, and security. By systematically mapping current innovations and identifying unresolved challenges—including standardization, clinical validation, and secure edge integration—this review positions reconfigurable architectures as a cornerstone for next-generation, patient-centric remote health monitoring. Future directions emphasize AI-enabled adaptability, sustainable and carbon-aware device design, and personalized healthcare through adaptive embedded systems, charting a pathway toward scalable and resilient telemedicine ecosystems.]]>
