Finger technique is a crucial aspect of piano learning, and hand exoskeleton mechanisms effectively assist novice piano players in maintaining correct finger technique consistently. Addressing current issues with exoskeleton robots, such as the inability to provide continuous correction of finger technique and their considerable weight, a novel hand exoskeleton robot has been developed to enhance finger technique through continuous correction and reduced weight. Initial data are gathered using finger joint angle sensors to analyze movements during piano playing, focusing on the trajectory and angular velocity of key strikes. This analysis informs the design of a 6-bar double-closed-loop mechanism with an end equivalent sliding pair, using analytical methods to establish the relationship between motor extension and input rod rotation. Simulation studies assess the exoskeleton’s motion space and dynamics, confirming its capability to meet structural and functional demands for accurate key striking. Prototype testing validates the exoskeleton’s ability to maintain correct finger positioning and mimic natural strike speeds, thus improving playing technique while ensuring comfort and safety.

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 applies cognitive psychology and deliberate practice theory, incorporating learners' musical abilities and the information entropy of piano works to design personalized practice pathways. Aiming to elucidate the mechanism linking intrinsic motivation and performance state in piano deliberate practice, the study addresses the empirical gap in quantifying task complexity and ability-task matching. Ninety undergraduate piano majors participated in a 28-day pretest-intervention-posttest experiment, with practice tasks assigned based on Musical Ear Test (MET) scores and the entropy-based classification of piano pieces. Psychological measures included intrinsic motivation (IMI, GSES, PCS), performance state (AGQ-R), and deliberate practice behaviors (DPQ). Results indicated that higher ability-task matching significantly enhanced intrinsic motivation and performance state, with increased self-efficacy, perceived competence, and mastery-oriented goals, as well as reduced avoidance tendencies. Mediation analysis further demonstrated that deliberate practice partially mediated the relationship between intrinsic motivation and performance state (R² = 0.01, p < 0.001), and high ability-entropy matching facilitated the coordinated development of cognitive, emotional, and behavioral components. This study is the first to introduce information entropy from information theory into piano pedagogy, providing empirical support for ability-task matching and deliberate practice as effective mechanisms to enhance motivation, resilience, and performance stability in music education.