Chronic obstructive pulmonary disease (COPD) causes about 3 million deaths annually and significantly burdens healthcare systems, costing the EU 38.6 billion euros, largely due to frequent hospitalizations triggered by acute exacerbations (AECOPD). AECOPD worsens patient health, accelerates lung decline, and lowers quality of life, highlighting the need for early detection. Moreover, these AECOPD events happen in an out-hospital setting and are therefore, not preventable. A clear clinical and quality-of-life need arises to reduce AECOPD-related events and consequent hospitalizations. Mobile health (mHealth) offers a solution by monitoring patients remotely using unobtrusive wearable devices. Parameters like peripheral oxygen saturation (SpO2) and respiratory rate can detect and predict exacerbations. However, no data at home is available of AECOPD events and robust predictive algorithms are lacking. This study aims to monitor vital parameters at home, tracking physical activity, pulse, respiratory rate, SpO2, sleep, and skin temperature from the moment of ER admission until three months post-discharge. Data will be used to gain insight in the COPD progression following an AECOPD event and to construct a predictive model, enabling timely intervention, reducing hospitalizations, and improving outcomes.