This paper presents an automated, end-to-end TinyML framework that streamlines the training, deployment, and on-device evaluation of machine-learning models on WeBe Band, a research-grade, healthcare-focused wearable device developed by Health-eTile. The WeBe Band integrates multimodal physiological and motion sensors and provides a practical platform for evaluating edge AI under realistic wearable constraints. By abstracting embedded firmware complexity and enabling rapid over-the-air deployment and profiling, the proposed system allows researchers to efficiently explore trade-offs between model complexity, inference latency, and memory usage on microcontroller-class hardware. Experimental results show that lightweight classical models achieve reliable real-time performance with minimal resource overhead, highlighting the effectiveness of the framework for wearable edge AI applications.