Bridging Clinical Needs and AI in Post-Stroke Rehabilitation: Patient Grouping, Adaptive Interventions, and Prognostic Assessment

The integration of AI into robotic rehabilitation holds promise for enabling adaptive and personalized therapy protocols based on individual motor and cognitive profiles. This paper outlines the conceptual design of an AIenhanced assessment and rehabilitation framework for stroke built on the TIAGo robotic platform. The protocol guides patients through functional gestures-such as reaching and hand-to-mouth movements-while collecting multimodal data via onboard sensors, depth cameras, and vocal interaction. AI applications are envisioned in three key domains: patient clustering and classification based on motor and cognitive indicators; real-time movement analysis for dynamic task adaptation based on parameters such as reaction time, range of motion, and spatial patterns; and outcome prediction using integrated kinematic, EMG, and EEG data. Although still under development, the proposed framework incorporates realistic patient clustering examples, grounded in clinical experiences, to illustrate potential stratification strategies and adaptation pathways. The paper aims to contribute to the ongoing discussion on how AI can enhance rehabilitation robotics by informing protocol development and supporting future clinical research.