Rehabilitative therapies play a crucial role in upper limb motor recovery, as upper limbs are the most active parts in executing the activities of daily living. Because of a huge number of people with motor disorders and a shortage of therapists, the integration of data-driven Artificial Intelligence methodologies and robots for rehabilitation could be helpful in creating personalized and challenging therapies, leading to a myriad of benefits for both patients and therapists. Artificial Intelligence methods can be implemented in different functional modules of the robotic platform, such as user intention recognition, robot motion planning, robot interaction control, and system adaptation through different learning paradigms. This paper presents a systematic literature review on the use of data-driven learning methods applied in upper limb robot-aided rehabilitation. The analysis is structured around the learning paradigms adopted, namely, supervised, unsupervised, and reinforcement learning, as well as the corresponding task types (e.g., classification, regression, and control tasks) and model types, distinguishing between machine learning and deep learning approaches. The review reveals that most studies employ supervised learning to address classification tasks, and that deep learning models are the most frequently adopted.
Loredana Zollo (2025) studied this question.