While generative artificial intelligence (Gen AI) is accelerating digital transformation and innovation in corporate product design (CPD), limited research has explored how designers adopt this technology. This study aims to identify the key factors and causal configurations that influence designers’ intentions to adopt Gen AI in CPD. This study involved 327 in-service designers as participants, employed semi-structured interviews and a questionnaire to collect data, and applied the grounded theory and fsQCA to analyze the data. The findings indicate the following: (1) Personal innovativeness, AI technological anxiety, perceived usefulness, task–technology fit, perceived risk, social influence, and organizational support are the key factors influencing designers’ adoption of Gen AI. (2) None of these factors constitute a necessary condition for designers to adopt Gen AI. (3) High adoption intention results from the interaction of multiple factors, which can be categorized into three driving logics: “task demand-driven”, “organizational environment-driven”, and “individual characteristics-driven”. It is recommended that corporate managers establish an AI training framework, foster a supportive organizational environment, and implement tailored strategies to facilitate the integration of new technologies. This study clarifies the factors influencing designers’ adoption of Gen AI in CPD and provides a framework for companies to effectively integrate AI systems into product design.

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.

The gait rehabilitation knee exoskeleton is an advanced rehabilitative assistive device designed to help patients with knee joint dysfunction regain normal gait through training and activity support. This paper introduces a design framework based on the process knowledge representation method to optimize the design and control efficiency of the knee exoskeleton. This framework integrates knowledge of design objects and processes, specifically including requirements, functions, principle work areas, and the representation and multi-dimensional dynamic mapping of the Behavior–Structure (RFPBS) matrix, achieving multi-dimensional dynamic mapping of the knee exoskeleton. This method incorporates biomechanical and physiological knowledge from the rehabilitation process to more effectively simulate and support gait movements during rehabilitation. Research results indicate that the knee rehabilitation exoskeleton design, based on the RFPBS process knowledge representation model, accomplishes multi-dimensional dynamic mapping, providing a scientific basis and effective support for the rehabilitation of patients with knee joint dysfunction.

Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline that integrates behavior–emotion dual recognition, simulation verification, and parameter optimization with user demand mining, biomechanical simulation, and sustainable practices. The design utilizes GreenAI, focusing on low-power algorithms and eco-friendly materials, ensuring energy-efficient AI models and reducing the environmental footprint. A dual-channel data fusion method was developed, combining movement parameters from sit-to-lie transitions with emotional needs extracted from e-commerce reviews using the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) models. The fuzzy Kano model prioritized design objectives, identifying multi-position adjustment, joint protection, armrest optimization, and interaction comfort as key targets. An AnyBody-based human–device model quantified muscle (erector spinae, rectus abdominis, trapezius) and hip joint loads during posture changes. Simulations verified the design’s ability to improve load distribution, reduce joint stress, and enhance comfort. The optimized nursing bed demonstrated improved adaptability across user profiles while maintaining functional reliability. This framework offers a scalable paradigm for intelligent rehabilitation equipment design, with potential extension toward AI-driven adaptive control and clinical validation. This sustainable methodology ensures that the device not only meets rehabilitation goals but also contributes to a more environmentally responsible healthcare solution, aligning with global sustainability efforts.

This study developed and evaluated a novel upper-limb rehabilitation device that integrates piano playing into task-oriented occupational therapy, addressing the limitations of traditional continuous passive motion (CPM) training in patient engagement and functional recovery. The system features a bi-axial sliding platform for precise 61-key positioning and a ten-link, four-loop robotic hand for key striking. A hierarchical control framework incorporates MIDI-based task mapping, finger optimization using an improved Hungarian algorithm, and impedance–admittance hybrid control for adaptive force–position modulation. An 8-week randomized controlled trial demonstrated that the experimental group significantly outperformed the control group, with a 74.7% increase in Fugl–Meyer scores (50.5 ± 2.5), a 14.6-point improvement in the box and block test (BBT), a 20.2-s reduction in nine-hole peg test (NHPT) time, and a 72.6% increase in rehabilitation motivation scale (RMS) scores (55.4 ± 3.8). The results indicate that combining piano playing with robotic rehabilitation enhances neuroplasticity and engagement, significantly improving motor function, daily activity performance, and rehabilitation adherence. This mechanical-control synergy introduces a new paradigm for music-interactive rehabilitation, with potential applications in home-based remote therapy and multimodal treatment integration.

Healthcare services and rehabilitation equipment have entered a phase of rapid development driven by user requirements. However, the development of corresponding design models for rehabilitation equipment is lacking. A general framework and development process are urgently needed for neurorehabilitation physiotherapy equipment. To address problems such as inadequate knowledge representation in the design process and difficulties in modeling the functional structure of the product, we combined a decomposition topology model of neurorehabilitation physiotherapy equipment with the modular design method. We proposed a general model for the design of neurorehabilitation physiotherapy equipment comprising the following modules: requirements (R), function (F), principle workspace (W), behavior (B), and structure (S), i.e., the RFWBS model. Using the neurorehabilitation robotic glove as an example for design practice, in terms of kinematics, the mechanism is analyzed by establishing a Lagrangian coordinate system for resolution. The mechanism has three degrees of freedom and can achieve the natural flexion and extension angles of each finger joint. In terms of dynamics, during the entire finger extension, the angular acceleration is almost zero, and the average angular velocity is approximately 30~50°/s. This indicates that the mechanism is suitable for wearable use, validating the scientific and effective nature of the RFWBS expanded model.