Automated pain monitoring devices are critical for effective patient care, particularly for non-communicative individuals in intensive care and post-operative settings. This paper presents the development of a medical device system for objective pain assessment that integrates facial expression recognition and surface electromyography (sEMG) signal processing. The system is designed as a clinically-deployable device featuring a nine-level hierarchical warning mechanism for real-time pain monitoring. The device architecture consists of three modules: (1) a facial video acquisition unit using standard webcam technology, (2) a wireless sEMG signal recording unit, and (3) a central processing unit implementing a hybrid fusion model. The fusion model dynamically weights facial expression indicators (derived from Facial Action Coding System analysis) and sEMG features (time-domain Root Mean Square and frequency-domain Mean Power Frequency) to determine pain severity. Experimental validation with 45 subjects demonstrated an overall accuracy of 95.6\%, significantly outperforming unimodal approaches (93.3\% for both facial-only and sEMG-only configurations). The device achieves real-time processing with an average latency of 1.2 seconds, meeting clinical deployment requirements. This medical device system offers a cost-effective, automated solution for continuous pain monitoring, facilitating timely medical intervention and enhancing precision care capabilities in healthcare environments.