In recent years, intelligent rehabilitation technology has advanced significantly, leveraging multimodal sensing, data fusion, and both endogenous and exogenous stimulation to enhance neurorehabilitation and human-machine interaction. Integrating multimodal information feedback—such as bioelectrical signals, kinematic data, and physiological responses—enhances movement intention recognition and optimizes rehabilitation interventions. Rehabilitation robotics and neurorehabilitation technologies now incorporate adaptive control strategies, personalized feedback, and multimodal stimulation to promote motor recovery and neural plasticity. By integrating artificial intelligence, biomechanics, and neuroscience, these innovations enable more precise, responsive, and effective rehabilitation solutions. However, challenges remain in optimizing multimodal data fusion, ensuring real-time interaction, and enhancing system adaptability. This Research Topic aims to explore cutting-edge methods and technologies that integrate multimodal sensing, intelligent control, and feedback mechanisms to advance rehabilitation outcomes, with an emphasis on novel algorithms, system designs, and theoretical advancements in intelligent rehabilitation.

The goal of this Research Topic is to advance intelligent rehabilitation by leveraging multimodal information feedback and stimulation to address the limitations of conventional rehabilitation methods. Traditional approaches, such as repetitive physical therapy, often lack real-time adaptability and personalization, leading to slow and inconsistent recovery. A key challenge is the limited ability to interpret real-time patient data and dynamically adjust rehabilitation strategies. To overcome this, recent advances in multimodal sensing, data fusion, and stimulation technologies provide a more comprehensive approach to neurorehabilitation. By integrating bioelectrical signals (EMG, EEG), motion data, biomechanical feedback, and both endogenous and exogenous stimulation, intelligent rehabilitation systems can enhance movement intention recognition and optimize therapy interventions. Machine learning and neural decoding techniques enable real-time processing of multimodal data, improving movement prediction and adaptive control. Additionally, robotic exoskeletons and neurostimulation techniques can dynamically adjust therapy based on personalized feedback, promoting motor recovery and neural plasticity.

Achieving clinical integration requires further advancements in sensor fusion algorithms, adaptive control mechanisms, and bidirectional human-machine interaction. This Research Topic aims to explore novel multimodal information feedback and stimulation strategies to enhance rehabilitation outcomes and improve the efficacy of intelligent rehabilitation systems. The Research Topic "Intelligent Rehabilitation Technology Incorporating Multimodal Information Feedback and Stimulation" focuses on advancing rehabilitation through the integration of innovative technologies. Contributors are invited to explore the following key themes:
1. Development of multimodal sensing systems for accurate movement intention detection.
2. Algorithms for multimodal data fusion to enhance rehabilitation precision.
3. Machine learning-driven adaptive rehabilitation interventions for personalized therapy.
4. Human-machine interaction in intelligent rehabilitation systems.
5. Biomechanical and neurophysiological studies guiding rehabilitation technology development.

Multimodal endogenous and exogenous stimulation techniques for neurorehabilitation.
We welcome original research articles and systematic reviews that emphasize interdisciplinary collaboration, novel methodologies, and system design. Contributions that push the boundaries of multimodal information feedback, intelligent control, and adaptive stimulation are especially encouraged to drive the evolution of intelligent rehabilitation technology.

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Intelligent Rehabilitation Technology Incorporating Multimodal Information Feedback and Stimulation