TY - JOUR
T1 - Deep Reinforcement Learning with Inverse Reinforcement Learning for Learning Optimal Personalized Rehabilitation Exercise Prescriptions from Physical Therapist Demonstrations
AU - Lucas Fernandez
AU - Diego Martinez
AU - Pablo Ruiz
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2026
VL - 5
IS - 2
SP - 139
N2 - Personalized rehabilitation exercise prescriptions are essential for recovery after neurological injury, orthopedic surgery, and chronic decline. While physical therapists have valuable expertise, translating it into scalable computational systems is challenging. Standard deep reinforcement learning relies on manually defined reward functions, but in rehabilitation, clinically significant goals like movement quality, fatigue, pain, safety, motivation, and adherence are difficult to quantify. This paper introduces a framework combining inverse reinforcement learning (IRL) and deep reinforcement learning (DRL) to learn personalized rehabilitation prescriptions from therapist demonstrations. IRL would derive expert-aligned rewards, and DRL would use these to create adaptive exercise plans. The framework encompasses therapist demonstration collection, movement trajectory representation, reward inference, policy learning, safety constraints, and clinical oversight. Demonstrations would include exercise selection, progression decisions, and therapist responses to patient fatigue, pain, or adherence issues. IRL could capture implicit clinical priorities, while DRL would adjust prescriptions based on patient conditions such as fatigue, progress, and engagement. The framework aims to create scalable, personalized rehabilitation prescriptions, offering a conceptual model for future rehabilitation robotics, exergaming, and home-based digital rehabilitation systems.
UR - https://cirpublications.com/j881480578
ER -