The 2019 Coronavirus disease-2019 (COVID-19) is a novel disease that affects multiple body organs. Several details about its long-term effects are unknown. Follow-up and early detection of post-COVID complications could help improve health conditions of post COVID-19 patients who have recovered. Clinical staff often use an early -warning system indicators to detect if individuals who are at risk for further complication. This study aims to design an Early Detection Algorithm (EAD) system that can help predict post COVID-19 physiological complications based on symptoms gathered by wearable devices in real time to alert patients of the possibility of developing complications and seek care or diagnostic testing. The system will monitor long term damage signaled by change of some physiological parameters which can lead to severe post-COVID-19 complications that may need hospitalization. The wearable device will monitor the patient’s body condition such as heart pulse rate, oxygen saturation level, body temperature, sleep pattern and then collected for processing. Consequently, when baseline parameters like heart rate, oxygen saturation etc. of a post COVID-19 patient are detected to have deviated an alert will be sent to the person taking care of the patient. Hence, this device will inform when the patient is advancing from mild to the moderate or serve condition of post COVID-19.