Soft pneumatic muscles for post-stroke lower limb ankle rehabilitation: leveraging the potential of soft robotics to optimize functional outcomes

Introduction: A soft pneumatic muscle was developed to replicate intricate ankle motions essential for rehabilitation, with a specific focus on rotational movement along the x-axis, crucial for walking. The design incorporated precise geometrical parameters and air pressure regulation to enable controlled expansion and motion.Methods: The muscle’s response was evaluated under pressure conditions ranging from 100-145 kPa. To optimize the muscle design, finite element simulation was employed to analyze its performance in terms of motion range, force generation, and energy efficiency. An experimental platform was created to assess the muscle’s deformation, utilizing advanced techniques such as high-resolution imaging and deep-learning position estimation models for accurate measurements. The fabrication process involved silicone-based materials and 3D-printed molds, enabling precise control and customization of muscle expansion and contraction.Results: The experimental results demonstrated that, under a pressure of 145 kPa, the y-axis deformation (y-def) reached 165 mm, while the x-axis and z-axis deformations were significantly smaller at 0.056 mm and 0.0376 mm, respectively, highlighting the predominant elongation in the y-axis resulting from pressure actuation. The soft muscle model featured a single chamber constructed from silicone rubber, and the visually illustrated and detailed geometrical parameters played a critical role in its functionality, allowing systematic manipulation to meet specific application requirements.Discussion: The simulation and experimental results provided compelling evidence of the soft muscle design’s adaptability, controllability, and effectiveness, thus establishing a solid foundation for further advancements in ankle rehabilitation and soft robotics. Incorporating this soft muscle into rehabilitation protocols holds significant promise for enhancing ankle mobility and overall ambulatory function, offering new opportunities to tailor rehabilitation interventions and improve motor function restoration

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques

Design of Micro-Bluetooth Motion Acquisition System

To realize the digitization of infant motion information and explore more abundant human health information with motion information to facilitate early treatment, a micro-Bluetooth motion acquisition system is designed. The low-power design of the micro-Bluetooth motion capture sensor in the system and intelligent algorithm for optimizing the precision of the infant movement measured angles can realize the system’s sustainable use and data reliability. With the help of collecting and analyzing human arm, leg, and head movement information, we can recognize that the system can carry out more research and experiments on natural infant movements

A design of lower limb rehabilitation robot and its control for passive training

Artificial intelligence-based rehabilitation robots can be applied for peoples with lower limb motor dysfunction usually caused by accident, war, sports, spinal cord injury, paralysis, and vascular diseases to enhance the motion ability of their lower limbs. A design of lower limb rehabilitation robot as well as its trajectory tracking which is suitable for passive training of patient with paralyzed or weak limbs have been presented in this paper. The contents were well organized and presented. Also, the experimental procedural setup of the lower limb rehabilitation robot is well described and conducted. Initially, AC servo motor was chosen as the actuator for controlling the robot due to good stability, fast response, low noise, wide speed range, low cost, high efficiency, and low maintenance. The corresponding simulation analysis of the actuator shows that it is good and can meet the design requirements. Secondly, the motion control of the robot was investigated using the kinematics model of the hip and knee transmission subsystem, the human hip and knee trajectory curves, the motion control data generation process, the planning target track, and the required servo motor drive pulse control instruction. Finally, the control experiments were conducted such that the pulse signal of the servo shaft rotational angle is received by the servo drive for pitch control in position control mode. By default, a complete rotation of 0.072 degrees corresponds to 500 pulses of the motor rotation time control. The experimental results show that by using PID controller, the actual trajectory is closer to the desired trajectory closely follows the actual trajectory and hence, demonstrated a promising patient's recovery