About this Research Topic
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
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
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