Rehabilitation is a complex process, which includes various interventions aimed at improving the dimensions of functioning, activity, and participation of individuals. Technological advances, particularly in artificial intelligence (AI) and robotics, are starting to be applied to support rehabilitation research and practice methods.
Intelligent mobile and wearable devices are available to collect data and provide users with information to assess health and monitor progress toward individualized rehabilitation goals. Moreover, socially and physically assistive systems are starting to be used to help people recuperate after illness or injury, or to help bridge gaps caused by sensory, motor, or cognitive impairments. The use of AI to improve the delivery and outcomes of rehabilitation is a priority of the National Institutes of Health, and, in the last decade, research on the use of AI to improve key rehabilitation processes and outcomes is steadily growing.
With the expansion of Deep Learning approaches, researchers have now started exploring the possibilities of jointly applying both Natural Language Processing (NLP) and Computer Vision (CV) approaches to improve the use of intelligent systems in several fields of application. Emerging advancements in these fields, as well as in the development of robotic hardware and accompanying algorithms, promise to improve the quality of care given to patients, while also providing economic benefits. Knowledge is built from health-related data that can be collected to assess patient outcomes in real-time, and even out of hospital settings within the environments that people live in.
The goal is to address AI and robotic applications that may improve the clinical workflow, increase patients’ safety, support diagnosis, and promote personalized treatment.
This Research Topic is intended to provide an overview of the research being carried out in both the areas of AI and robotics to support rehabilitation practice. As this integration requires an interdisciplinary attitude, the Research Topic aims to gather researchers with broad expertise in various fields such as machine learning, computer vision, natural language, bioengineering, neuroscience, medicine, and rehabilitation science, to discuss their cutting-edge work and perspectives on future directions in this emerging application field. Original Research Articles, Reviews, and Perspectives contributions addressing the following themes are sought, covering the whole range of theoretical and practical aspects, technologies, and systems:
• NLP techniques and methods supporting human-robot interactions in clinical contexts
• CV techniques and methods supporting motion analysis
• AI for decision support systems
• AI frameworks supporting clinical services
• Deep Learning for clinical assessment and outcome monitoring.
Rehabilitation is a complex process, which includes various interventions aimed at improving the dimensions of functioning, activity, and participation of individuals. Technological advances, particularly in artificial intelligence (AI) and robotics, are starting to be applied to support rehabilitation research and practice methods.
Intelligent mobile and wearable devices are available to collect data and provide users with information to assess health and monitor progress toward individualized rehabilitation goals. Moreover, socially and physically assistive systems are starting to be used to help people recuperate after illness or injury, or to help bridge gaps caused by sensory, motor, or cognitive impairments. The use of AI to improve the delivery and outcomes of rehabilitation is a priority of the National Institutes of Health, and, in the last decade, research on the use of AI to improve key rehabilitation processes and outcomes is steadily growing.
With the expansion of Deep Learning approaches, researchers have now started exploring the possibilities of jointly applying both Natural Language Processing (NLP) and Computer Vision (CV) approaches to improve the use of intelligent systems in several fields of application. Emerging advancements in these fields, as well as in the development of robotic hardware and accompanying algorithms, promise to improve the quality of care given to patients, while also providing economic benefits. Knowledge is built from health-related data that can be collected to assess patient outcomes in real-time, and even out of hospital settings within the environments that people live in.
The goal is to address AI and robotic applications that may improve the clinical workflow, increase patients’ safety, support diagnosis, and promote personalized treatment.
This Research Topic is intended to provide an overview of the research being carried out in both the areas of AI and robotics to support rehabilitation practice. As this integration requires an interdisciplinary attitude, the Research Topic aims to gather researchers with broad expertise in various fields such as machine learning, computer vision, natural language, bioengineering, neuroscience, medicine, and rehabilitation science, to discuss their cutting-edge work and perspectives on future directions in this emerging application field. Original Research Articles, Reviews, and Perspectives contributions addressing the following themes are sought, covering the whole range of theoretical and practical aspects, technologies, and systems:
• NLP techniques and methods supporting human-robot interactions in clinical contexts
• CV techniques and methods supporting motion analysis
• AI for decision support systems
• AI frameworks supporting clinical services
• Deep Learning for clinical assessment and outcome monitoring.