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

AI is almost completely worthless until the underlying research for 100% recovery is there!  You're putting the cart before the horse! 

What absolute stupidity; survivors WANT 100% RECOVERY NOT this! Do your ever actually talk to survivors? All this is doing is suggesting useless guidelines, which don't EXACTLY DELIVER RECOVERY! Survivors want EXACT PROTOCOLS!

Well I'd suggest dumping all stroke research into Dr. Watson of IBM and see what comes out.

dr. Watson (55 posts to April 2012)

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

t Adriano Scibilia1,†, Giorgia Gatto2,†, Alessandro Brusaferri1, Matteo Lancini2 and Marco Caimmi1,* 1National Research Council of Italy, Via Alfonso Corti, 12- 20133 Milano, Italy 2University of Brescia, Via Branze 43- 25123 Brescia, Italy 

 Abstract 

 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 AI enhanced assessment and rehabilitation framework for stroke built on the TIAGo robotic platform. The protocol(That's not really a protocol because there is nothing EXACT there!) 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.