Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such applications at the edge, where limited computing resources constrain the design space. This paper introduces a novel approach to AI-driven personalized solutions in biosensing applications by combining deep learning with clustering-based separation techniques. The proposed Clustering and Learning for Emotion Adaptive Recognition (CLEAR) methodology strikes a balance between population-wide models and fully personalized systems by leveraging data-driven clustering. CLEAR demonstrates its effectiveness in emotion recognition tasks, and its integration with fine-tuning enables efficient deployment on edge devices, ensuring data privacy and real-time detection when new users are introduced to the system. We conducted experiments for model personalization on two edge computing platforms: the Coral Edge TPU Dev Board and the Raspberry Pi with an Intel Movidius Neural Compute Stick 2. The results show that initial cluster assignment for new users can be achieved without labeled data, directly addressing the cold-start problem. Compared to baseline validation without clustering, this proposal improves accuracy metric from 75% to 81.9%. Furthermore, fine-tuning with minimal labeled data significantly improves accuracy, achieving up to 86.34% for the fear detection task in the WEMAC dataset while remaining suitable for deployment on resource-constrained edge devices.