Fatigue, pain, and stress are central to rehabilitation science, presenting significant hurdles that affect both patient outcomes and well-being. With technology becoming an integral part of healthcare, physiologic monitoring, such as electrocardiography (ECG) and electroencephalography (EEG), alongside subjective assessments using psychometric tools, provide data-rich avenues for exploring these challenges. Recent studies have indicated the feasibility of using cardiac and respiratory signal measurements at home, compared to more complex EEG metrics. However, to extract pertinent data and multi-modal signals using minimal sensors, there is a pressing need for the development of new techniques and algorithms that maximize their utility and adaptation to patient environments. Despite advancements, integrated methodologies for a wide spectrum of neurological conditions, such as stroke, multiple sclerosis, Parkinson's disease, traumatic brain injuries, and spinal cord injury, remain underexplored.
This Research Topic aims to dive into the integration of comprehensive assessment models for managing fatigue, pain, and stress in rehabilitation. By leveraging interdisciplinary research, the objective is to investigate how these evaluation methods can optimize personalized treatment, improve clinical outcomes, and refine rehabilitation strategies. Key aims include the illumination of novel therapeutic frameworks, enhancement of integrated assessment tools, and the facilitation of interdisciplinary insight into the management of these interrelated factors. The pursuit of well-rounded approaches, such as combining non-pharmacological with pharmacological methods along with machine learning and advanced sensor technologies, will be critical to the exploration and development of personalized rehabilitation protocols.
To gather further insights into the integrated assessment of rehabilitation challenges, we welcome articles addressing, but not limited to, the following themes: - Physics-Based Models in Rehabilitation Science - Nonlinear Analysis of Physiological Signals - Integration of Multimodal Data for Personalized Rehabilitation - Development of novel therapeutic frameworks and assessment tools - Machine learning for predictive modeling in rehabilitation settings
Please note that we invite submissions encompassing original research, reviews, and opinion pieces that align with these themes, aiming for a combination of evidence-based inquiry and practical application in clinical rehabilitation settings.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
Original Research
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
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.
