This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy and daily activity assistance, challenges remain in performing highly dexterous tasks due to structural complexity and insufficient motion accuracy. To address these issues, we developed a modular division method based on multi-domain mapping and a top-down process model. This method integrates the functional domain, structural domain, and user needs domain, and explores the principles and methods for creating functional construction modules, overcoming the limitations of traditional top-down approaches in design flexibility. By closely combining layout constraints with the design model, this method significantly improves the accuracy and efficiency of module configuration, offering a new path for the development of piano practice assistance devices. The results demonstrate that this device innovatively combines piano practice with rehabilitation training and through the introduction of ontological modeling methods, resolves the challenges of multidimensional needs mapping. Based on five user requirements (P), we calculated the corresponding demand weight (K), making the design more aligned with user needs. The device excels in enhancing motion accuracy, interactivity, and comfort, filling the gap in traditional piano assistance devices in terms of multi-functionality and high adaptability, and offering new ideas for the design and promotion of intelligent assistive devices. Simulation analysis, combined with the motion trajectory of the finger’s proximal joint, calculates that 60° is the maximum bending angle for the aforementioned joint. Physical validation confirms the device’s superior performance in terms of reliability and high-precision motion reproduction, meeting the requirements for piano-assisted training. Through multi-domain mapping, the top-down process model, and modular design, this research effectively breaks through the design flexibility and functional adaptability bottleneck of traditional piano assistance devices while integrating neurological rehabilitation with music education, opening up a new application path for intelligent assistive devices in the fields of rehabilitation medicine and arts education, and providing a solution for cross-disciplinary technology fusion and innovative development.

This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, and loss of motor coordination during rehabilitation. To achieve this, it proposes an innovative design method for a lower limb rehabilitation training system based on Bayesian networks and parallel mechanisms. A Bayesian network model is constructed based on expert knowledge and structural mechanics analysis, considering key factors such as rehabilitation scenarios, motion trajectory deviations, and rehabilitation goals. By utilizing the motion characteristics of parallel mechanisms, we designed a rehabilitation training device that supports multidimensional gait correction. A three-dimensional digital model is developed, and multi-posture ergonomic simulations are conducted. The study focuses on quantitatively assessing the kinematic characteristics of the hip, knee, and ankle joints while wearing the device, establishing a comprehensive evaluation system that includes range of motion (ROM), dynamic load, and optimization matching of motion trajectories. Kinematic analysis verifies that the structural design of the device is reasonable, aiding in improving patients’ gait, enhancing strength, and restoring flexibility. The Bayesian network model achieves personalized rehabilitation goal optimization through dynamic probability updates. The design of parallel mechanisms significantly expands the range of joint motion, such as enhancing hip sagittal plane mobility and reducing dynamic load, thereby validating the notable optimization effect of parallel mechanisms on gait rehabilitation.