The integration of technologies for collecting physiological data via wearable devices has introduced new ways to enhance User Experience (UX) evaluation also in terms of understanding users’ emotions. By exploring emotional responses, UX professionals can create more adaptive, intuitive, satisfying, and emotionally aligned experiences that better meet users’ needs. With this perspective, our study aimed to investigate the benefits and limitations of using a low-cost wristband to predict emotions. For this purpose, we developed an Android application for managing physiological data collected through an Empatica E4 wristband and employed machine-learning techniques for emotion classification of such data. We then conducted a user study with 30 participants who viewed seven videos stimulating specific emotions, and we compared the device and application results with the subjects’ self-reported emotional states. The results provide useful information to better understand the possibilities and limitations when using a wearable device for emotional monitoring.