Skip to main content

About this Research Topic

Manuscript Submission Deadline 27 March 2024

This Research Topic is still accepting articles. For authors aiming to contribute, please submit your manuscript today

Robot-aided rehabilitation has witnessed significant advancements in recent years, aiming to enhance the recovery process for individuals with physical impairments. The development of multimodal interfaces for patient monitoring integrated to gather real-time information about the human-robot interaction is crucial. These interfaces enable the collection and analysis of data from multiple modalities such as vision, force and motion, providing valuable insights into the patient's progress and motor performance. Artificial intelligence (AI) started exploring approaches to further augment the capabilities of these interfaces. By leveraging AI algorithms for data processing, pattern recognition and predictive analytics, multimodal interfaces enable robots to offer personalized and adaptive feedback, optimizing therapy protocols and quantifying the rehabilitation outcomes. Objective assessment aids in treatment planning, but also promotes personalized robot interventions tailored to each individual's specific needs. Furthermore, monitoring in rehabilitation robotics allows large-scale dataset building, facilitating collaborative research, benchmarking and the development of evidence-based practices.

This Research Topic focuses on addressing the problem of developing AI-integrated multimodal interfaces for monitoring during robot-aided rehabilitation to tailor patient-specific intervention. The key challenge lies in optimizing the multimodal monitoring process to provide real-time feedback and personalized assistance, leading to improved rehabilitation outcomes. The fusion of multiple modalities, including vision, force, motion and physiological signals, through AI techniques, allows for a comprehensive understanding of the patient's condition. In this context, research can be carried out to advance the current state-of-the-art. Advancements in multimodal sensor development, sensor integration within rehabilitation robots, patients' state estimation algorithms and tailored control strategies collectively contribute to the development of AI-integrated multimodal interfaces. Innovations in sensor technologies improve accuracy, reliability, and real-time data acquisition, enabling the quantification of multimodal patients parameters. By incorporating sensors within robotic devices, it becomes possible to capture and analyze data about the human-robot interaction. The validation of methods for patient state estimation enables the deep understanding of the progresses during the rehabilitation process. Lastly, tailored control strategies aim to strike a balance between providing sufficient assistance and promoting active engagement for effective rehabilitation.

This Research Topic includes, but is not limited to, the following:
• Design and development of multimodal sensors (e.g. motion sensors, physiological monitoring sensors, force and EMG sensors, etc.)
• AI algorithms for real-time analysis and interpretation
• AI approaches for personalized feedback and adaptive interventions
• Tailored control strategies for optimizing assistance
• Human-robot interaction and user experience
• Data-driven modelling and prediction of robot-aided rehabilitation outcomes
• Analysis of ethical implications of AI-driven robot-aided rehabilitation

Keywords: Robot-aided rehabilitation, Multimodal monitoring, Tailored Systems User, State estimation algorithms, Real-time AI for Robots


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.

Robot-aided rehabilitation has witnessed significant advancements in recent years, aiming to enhance the recovery process for individuals with physical impairments. The development of multimodal interfaces for patient monitoring integrated to gather real-time information about the human-robot interaction is crucial. These interfaces enable the collection and analysis of data from multiple modalities such as vision, force and motion, providing valuable insights into the patient's progress and motor performance. Artificial intelligence (AI) started exploring approaches to further augment the capabilities of these interfaces. By leveraging AI algorithms for data processing, pattern recognition and predictive analytics, multimodal interfaces enable robots to offer personalized and adaptive feedback, optimizing therapy protocols and quantifying the rehabilitation outcomes. Objective assessment aids in treatment planning, but also promotes personalized robot interventions tailored to each individual's specific needs. Furthermore, monitoring in rehabilitation robotics allows large-scale dataset building, facilitating collaborative research, benchmarking and the development of evidence-based practices.

This Research Topic focuses on addressing the problem of developing AI-integrated multimodal interfaces for monitoring during robot-aided rehabilitation to tailor patient-specific intervention. The key challenge lies in optimizing the multimodal monitoring process to provide real-time feedback and personalized assistance, leading to improved rehabilitation outcomes. The fusion of multiple modalities, including vision, force, motion and physiological signals, through AI techniques, allows for a comprehensive understanding of the patient's condition. In this context, research can be carried out to advance the current state-of-the-art. Advancements in multimodal sensor development, sensor integration within rehabilitation robots, patients' state estimation algorithms and tailored control strategies collectively contribute to the development of AI-integrated multimodal interfaces. Innovations in sensor technologies improve accuracy, reliability, and real-time data acquisition, enabling the quantification of multimodal patients parameters. By incorporating sensors within robotic devices, it becomes possible to capture and analyze data about the human-robot interaction. The validation of methods for patient state estimation enables the deep understanding of the progresses during the rehabilitation process. Lastly, tailored control strategies aim to strike a balance between providing sufficient assistance and promoting active engagement for effective rehabilitation.

This Research Topic includes, but is not limited to, the following:
• Design and development of multimodal sensors (e.g. motion sensors, physiological monitoring sensors, force and EMG sensors, etc.)
• AI algorithms for real-time analysis and interpretation
• AI approaches for personalized feedback and adaptive interventions
• Tailored control strategies for optimizing assistance
• Human-robot interaction and user experience
• Data-driven modelling and prediction of robot-aided rehabilitation outcomes
• Analysis of ethical implications of AI-driven robot-aided rehabilitation

Keywords: Robot-aided rehabilitation, Multimodal monitoring, Tailored Systems User, State estimation algorithms, Real-time AI for Robots


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.

Topic Editors

Loading..

Topic Coordinators

Loading..

Articles

Sort by:

Loading..

Authors

Loading..

total views

total views article views downloads topic views

}
 
Top countries
Top referring sites
Loading..

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.