Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain–machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.

Prosthetic hands hold significant potential in aiding amputees to regain their manipulative abilities, instill confidence, and improve their quality of life. Nonetheless, the lack of compatibility between prosthetic devices and residual limbs could lead to secondary injuries, such as skin pressure ulcers caused by prolonged high-pressure states and restricted upper limb movements while using. These issues contribute to a high device abandonment rate among individuals with disabilities. In this study, a wearable monitoring system was initially designed and presented. Subsequently, multiindicators comprising motion deviation ( $\eta _{q}$ ), pressure distribution ( ${\eta _{p}}$ ), and proximity to pain pressure ( ${\eta _{\bar P}}$ ) were proposed, which were correspondingly derived from clinical criteria, pressure, and motion information. Then, a comprehensive assessment approach was proposed to evaluate the adaptability of prosthetic hands. Moreover, the proposed adaptability assessment approach based on the wearable monitoring system underwent testing on seven forearm amputees conducted under three loading conditions. The results were compared with a standard clinical scale, demonstrating a favorable evaluation performance (RMSE = 3.30, $R^{2} = 0.6019$ ). This research work enables quantitative adaptability evaluation of prosthetic hands and has the potential to aid in the optimization of prosthetic design.

Parkinson’s disease (PD) is one of the most common progressive neurodegenerative disorder, for which early detection and precise rehabilitation planning are essential to alleviate its impact on quality of life and reduce societal burden. Accurate, automated PD subtype classification and staging play a key role in designing effective rehabilitation strategies while minimizing reliance on intensive expert assessments. Unlike existing automated methods that typically depend on high–cost medical imaging (e.g., MRI) or extensive sensor networks, we introduce a low–cost motion measurement system employing only two inertial measurement units (IMUs) placed on the lower legs. We propose a Symbiotic Graph Attention Network (SGAT)–based algorithm that fuses node features and whole-body features for automated PD subtype and stage detection. By establishing a symbiotic mechanism between the subtype and staging tasks and using adaptive fusion weights, our method achieves outstanding performance—subtype accuracy of 0.91 and staging accuracy of 0.85—validated on data from 46 participants. Notably, the entire detection and recognition process requires merely a simple walking task and incurs minimal time cost. The system’s affordability, ease of use, and scalability underscore its substantial potential for large‑scale clinical deployment.

Prosthetic hands have significant potential to restore the manipulative capabilities and self-confidence of amputees and enhance their quality of life. However, incompatibility between prosthetic devices and residual limbs can lead to secondary injuries such as skin pressure ulcers and restricted joint motion, contributing to a high prosthesis abandonment rate. To address these challenges, this study introduces a data-driven design framework (D3Frame) utilizing a multi-index optimization method. By incorporating motion/ pressure data, as well as clinical criteria such as pain threshold/ tolerance, from various anatomical sites on the residual limbs of amputees, this framework aims to optimize the structural design of the prosthetic socket, including the Antecubital Channel (AC), Lateral Epicondylar Region Contour (LC), Medial Epicondylar Region Contour (MC), Olecranon Region Contour (OC), Lateral Flexor/ Extensor Region (LR), and Medial Flexor/ Extensor Region (MR). Experiments on five forearm amputees verified the improved adaptability of the optimized socket compared to traditional sockets under three load conditions. The experimental results revealed a modest score enhancement on standard clinical scales and reduced muscle fatigue levels. Specifically, the percent effort of muscles and slope value of mean/ median frequency decreased by 19%, 70%, and 99% on average, respectively, and the average values of mean/ median frequency in the motion cycle both increased by approximately 5%. The proposed D3Frame in this study was applied to optimize the structural aspects of designated regions of the prosthetic socket, offering the potential to aid prosthetists in prosthesis design and, consequently, augmenting the adaptability of prosthetic devices.

Currently, wearable systems have become mainstream in human motion analysis due to their low cost, minimal environmental constraints, and capability for prolonged monitoring. However, wearable inertial sensors can only directly measure motion data at their mounting locations, which necessitates increasing the number of sensors in the network to capture multi-limb data. This, in turn, diminishes the advantages of lightweight design and minimal motion interference. To address this issue, this paper proposes a dual-module driven indirect estimation method for foot orientation. The approach is based on two significant factors in foot orientation changes during gait: the direct driving from the shank and the foot’s intrinsic fine adjustment. Accordingly, two driving modules are constructed, and real-time adjustable weight factors are applied. Validation on 7 healthy subjects and 11 pathological subjects yielded a root mean square error (RMSE) of 3.81 ± 1.21 and an R value of 0.95. Compared with similar studies, the proposed method not only is applicable to healthy subjects but also demonstrates high robustness in pathological gait. Moreover, the hardware system, consisting solely of two inertial measurement units (IMUs) mounted on the shank, offers significant clinical value.

The multi-degree-of-freedom prosthetic control based on surface electromyogram (sEMG) is critical to enable flexible interaction between upper limb amputees and the environment. However, gestures and force levels are variables of each other and interfere with the accuracy of simultaneous recognition. In this letter, we propose a neural-driven simultaneous recognition approach of gestures and forces based on the motor unit (MU) discharges. The high-density surface electromyogram (HD-sEMG) signals were decomposed into the motor unit spike trains (MUSTs), and then the neural-driven features were extracted from the MUSTs by calculating the discharge count of each MU. The geodesic flow kernel (GFK) algorithm was proposed to enhance the neural-driven features and improve the accuracy of simultaneous recognition. Two random forest (RF) classifiers were used to predict gestures and force levels from the enhanced neural-driven features. The proposed MU-GFK approach was tested on eleven gestures and three force levels. The average accuracies of gesture and force recognition were 96.26% and 99.92%, respectively, superior to the conventional sEMG method. Moreover, the GFK algorithm improved the classification accuracy of gestures and forces by 0.98% and 3.5%, respectively. These results demonstrate the feasibility and superiority of the proposed MU-GFK approach, promoting the intelligent upper limb prosthesis to have the ability to precisely and simultaneously control gestures and forces, thus achieving more natural and intuitive human-machine-environment interaction.