Design and Development of a Wearable Exoskeleton System for Stroke Rehabilitation

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.

Improved Transfer Learning for Detecting Upper-Limb Movement Intention Using Mechanical Sensors in an Exoskeletal Rehabilitation System

The objective of this study was to propose a novel strategy for detecting upper-limb motion intentions from mechanical sensor signals using deep and heterogeneous transfer learning techniques. Three sensor types, surface electromyography (sEMG), force-sensitive resistors (FSRs), and inertial measurement units (IMUs), were combined to capture biometric signals during arm-up, hold, and arm-down movements. To distinguish motion intentions, deep learning models were constructed using the CIFAR-ResNet18 and CIFAR-MobileNetV2 architectures. The input features of the source models were sEMG, FSR, and IMU signals. The target model was trained using only FSR and IMU sensor signals. Optimization techniques determined appropriate layer structures and learning rates of each layer for effective transfer learning. The source model on CIFAR-ResNet18 exhibited the highest performance, achieving an accuracy of 95% and an F-1 score of 0.95. The target model with optimization strategies performed comparably to the source model, achieving an accuracy of 93% and an F-1 score of 0.93. The results show that mechanical sensors alone can achieve performance comparable to models including sEMG. The proposed approach can serve as a convenient and precise algorithm for human-robot collaboration in rehabilitation assistant robots.

Dynamic Analysis and Experimental Study of Lasso Transmission for Hand Rehabilitation Robot

Lasso transmission is a method for realizing long-distance flexible transmission and lightweight robots. However, there are transmission characteristic losses of velocity, force, and displacement during the motion of lasso transmission. Therefore, the analysis of transmission characteristic losses of lasso transmission has become the focus of research. For this study, at first, we developed a new flexible hand rehabilitation robot with a lasso transmission method. Second, the theoretical analysis and simulation analysis of the dynamics of the lasso transmission in the flexible hand rehabilitation robot were carried out to calculate the force, velocity, and displacement losses of the lasso transmission. Finally, the mechanism and transmission models were established for experimental studies to measure the effects of different curvatures and speeds on the lasso transmission torque. The experimental data and image analysis results show torque loss in the process of lasso transmission and an increase in torque loss with the increase in the lasso curvature radius and transmission speed. The study of the lasso transmission characteristics is important for the design and control of hand functional rehabilitation robots, providing an important reference for the design of flexible rehabilitation robots and also guiding the research on the lasso regarding the compensation method for transmission losses.

3D Printing Technology for Smart Clothing: A Topic Review

Clothing is considered to be an important element of human social activities. With the increasing maturity of 3D printing technology, functional 3D printing technology can realize the perfect combination of clothing and electronic devices while helping smart clothing to achieve specific functions. Furthermore, the application of functional 3D printing technology in clothing not only provides people with the most comfortable and convenient wearing experience, but also completely subverts consumers' perception of traditional clothing. This paper introduced the progress of the application of 3D printing from the aspect of traditional clothing and smart clothing through two mature 3D printing technologies normally used in the field of clothing, and summarized the challenges and prospects of 3D printing technology in the field of smart clothing. Finally, according to the analysis of the gap between 3D-printed clothing and traditionally made clothing due to the material limitations, this paper predicted that the rise in intelligent materials will provide a new prospect for the development of 3D-printed clothing. This paper will provide some references for the application research of 3D printing in the field of smart clothing.

The Middleware for an Exoskeleton Assisting Upper Limb Movement

This article presents the possibilities of newly developed middleware dedicated for distributed and modular control systems. The software enables the exchange of information locally, within one control module, and globally, between many modules. The executed information exchange system speed tests confirmed the correct operation of the software. The middleware was used in the control system of the active upper-limb exoskeleton. The upper-limb rehabilitation exoskeleton structure with six degrees of mechanical freedom is presented. The tests were performed using the prototype with three joints. The drives’ models of individual joints were developed and simulated. As a result, the courses of the motion trajectory were shown for different kinds of pressure on the force sensors, and different methods of signal filtering. The tests confirmed a correct operation of middleware and drives control system.

AI-Optimized Technological Aspects of the Material Used in 3D Printing Processes for Selected Medical Applications

While the intensity, complexity, and specificity of robotic exercise may be supported by patient-tailored three-dimensional (3D)-printed solutions, their performance can still be compromised by non-optimal combinations of technological parameters and material features. The main focus of this paper was the computational optimization of the 3D-printing process in terms of features and material selection in order to achieve the maximum tensile force of a hand exoskeleton component, based on artificial neural network (ANN) optimization supported by genetic algorithms (GA). The creation and 3D-printing of the selected component was achieved using Cura 0.1.5 software and 3D-printed using fused filament fabrication (FFF) technology. To optimize the material and process parameters we compared ten selected parameters of the two distinct printing materials (polylactic acid (PLA), PLA+) using ANN supported by GA built and trained in the MATLAB environment. To determine the maximum tensile force of the exoskeleton, samples were tested using an INSTRON 5966 universal testing machine. While the balance between the technical requirements and user safety constraints requires further analysis, the PLA-based 3D-printing parameters have been optimized. Additive manufacturing may support the successful printing of usable/functional exoskeleton components. The network indicated which material should be selected: Namely PLA+. AI-based optimization may play a key role in increasing the performance and safety of the final product and supporting constraint satisfaction in patient-tailored solutions.