Robotics is playing an increasingly vital role in healthcare, particularly through systems that monitor, assist, and respond to patient needs. A key enabler of this transformation is the integration of biosignals and multimodal perception—leveraging physiological data such as heart rate, EEG, EMG, and thermal imaging to adapt robot behavior in real time. These signals, when fused with visual, auditory, and contextual information, allow healthcare robots to provide personalized, safe, and effective interventions. However, challenges remain in standardizing biosignal interpretation, real-time data fusion, and ensuring robustness in clinical and home environments.
The primary goal of this Research Topic is to explore how multimodal biosignal integration can enhance the adaptability and intelligence of healthcare robots. While physiological sensing technologies have advanced rapidly, their effective incorporation into robotic systems remains an open challenge due to variability, noise, and a lack of standardized frameworks. This collection aims to bring together interdisciplinary efforts that address technical, clinical, and practical challenges in using biosignals to drive real-time robotic perception and decision-making.
This Research Topic welcomes original research, reviews, and application-focused case studies on the use of biosignal and multimodal perception for healthcare robotics. Topics include, but are not limited to: • Biosignal acquisition and interpretation in robotic systems • Multimodal data fusion for adaptive robot behavior • Emotion and stress detection in patient–robot interactions • Physiological-aware human–robot collaboration • Personalized rehabilitation using biosignal-driven feedback • Signal quality enhancement, noise mitigation, and standardization efforts • Clinical validations and deployment in real-world settings
Submissions should emphasize practical applications, robustness, and translational potential in healthcare environments. Cross-disciplinary research combining robotics, biomedical engineering, machine learning, and clinical sciences is strongly encouraged.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
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FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
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