Artificial Intelligence and Multimodal Data in Neural Rehabilitation

Dear Colleagues,

The interdisciplinarity in research fields has led to the use of engineering and computer sciences in medicine, taking advantage of the latter in diagnostic, prognostic, and therapeutic tools.

The various opportunities made available by bio-engineering and robotics, traditionally used for rehabilitation, allow a precise, accurate, and objective diachronic quantitative monitoring of the subjects’ behaviors. The activity enables us to identify peculiarities related to individual patients to personalize the treatment and optimize its effectiveness and efficiency.

The extracted data are multimodal and, because of their complexity, require the guidance of expert clinicians to interpret them and automated calculations to analyze them from a clinical point of view adequately. Functional evaluation indices also determine the findings to be processed objectively.

Such a scenario opens up new challenges that require the collaboration of the entire scientific community. For this reason, it is essential to collect interdisciplinary research contributions for a free and fruitful comparison.

For this purpose, we have identified the following topics which are indicative and not exclusive:

• Cognitive and motor rehabilitation;
• Cognitive impairment assessment and training;
• Motor impairment assessment and training;
• Motor learning processes and neuroplasticity;
• Dual-task training;
• Electrophysiological signals;
• Multimodal data processing and data fusion;
• Time series analysis (1-D, 2-D, and n-D);
• Indexes in multimodal data analysis;
• Assessment indexes in clinical applications (prognosis, diagnosis, treatment, follow-up);
• Intelligent systems and smart objects for cognitive rehabilitation;
• Robotic rehabilitation systems and sensors enhanced by artificial intelligence;
• Innovative Human–machine Interfaces (HMI) and AI-based gesture analysis;
• Adaptive and personalized rehabilitation protocols based on artificial intelligence;
• Methodologies for assessing and measuring the impact of rehabilitation based on artificial intelligence.

Dr. Marco Righi
Dr. Cristina Dolciotti
Dr. Paolo Bongioanni
Guest Editors

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• cognitive rehabilitation
• motor rehabilitation
• neural rehabilitation
• robotics for healthcare
• multimodal analysis
• time series
• assessment indexes
• artificial intelligence
• machine learning