About this Research Topic
Owing to the advancement of computational technology as well as the ubiquitous nature of sensor technology, the employment of artificial intelligence, particularly machine learning and its associative architectures (deep learning, reinforcement learning, amongst others) in facilitating the act of rehabilitation is non-trivial. The employment of such a technique is increasingly used to develop personalised treatment plans for patients, as well as to improve the accuracy of diagnoses and prognoses. Consequently, this does not only improve the effectiveness of treatment, but also allows for more efficient and cost-effective care.
This Research Topic is dedicated to cater for any form of contribution or advances concerning the utilisation of machine learning to the broader domain of rehabilitation sciences.
We welcome original articles, briefs/short communications as well as systematic literature reviews, of the following (but not limited to):
Precision Medicine/Rehabilitation
Rehabilitation Intervention
Functional Recovery Prediction
Injury Risk Assessment
Smart Health Monitoring System
Assistive Technology
Rehabilitation Psychology
Speech-language Pathology
This Research Topic is dedicated to cater for any form of contribution or advances concerning the utilisation of machine learning to the broader domain of rehabilitation sciences.
We welcome original articles, briefs/short communications as well as systematic literature reviews, of the following (but not limited to):
Precision Medicine/Rehabilitation
Rehabilitation Intervention
Functional Recovery Prediction
Injury Risk Assessment
Smart Health Monitoring System
Assistive Technology
Rehabilitation Psychology
Speech-language Pathology
Keywords: Deep Learning, Machine Learning, Rehabilitation Sciences, Data Analytics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
