Accurate pain assessment remains one of the more persistent challenges in clinical research and practice. Most existing approaches depend on patients recalling and reporting pain after the fact — a process prone to memory distortion, inconsistent follow-through, and an absence of physiological context. This project proposes a wearable-based platform that captures pain data in real time, rather than relying on retrospective accounts. The system pairs a Samsung Galaxy Watch, running a purpose-built Wear OS application, with a companion Android smartphone app. IBS patients can log pain intensity via a visual analog scale while heart rate, photoplethysmography (PPG), and electrodermal activity (EDA) simultaneously records. These signals provide physiological context beyond self-report alone. Data is transmitted to the smartphone via the Wear OS Data Layer API and stored locally for downstream analysis. Validation was carried out through controlled experiments targeting both chronic and acute pain responses. Acute pain responses were induced thermally using a thermogrill pain inducer, while mechanical pressure was applied via an algometer to simulate chronic pain conditions — both methods enabling precise, repeatable stimulus delivery. This framework allowed evaluation of how reliably the system captures alignment between subjective pain ratings and concurrent physiological changes. Usability and reliability were treated as primary constraints throughout development. The watch interface was streamlined for quick, low-effort logging that minimizes user disruption, while backend processes handle device synchronization automatically. The result is a platform practical enough for everyday use, yet capable of generating the high-resolution, multi-modal data needed to meaningfully advance how pain is studied — in both controlled and real-world settings.