Elbow joint rehabilitation presents a formidable challenge, underscored by the
joint’s complex biomechanics and high vulnerability to injuries and degenerative
conditions. Despite the advancements in rehabilitative technology, current
solutions such as rigid exoskeletons often fall short in providing the precision,
flexibility, and customization needed for effective treatment. Although traditional
robotic aids, such as rigid exoskeletons, help recover, they lack in providing
sufficient flexibility, comfort, and easy customization with no need for
complicated calculation and complex design considerations. The introduction
of soft pneumatic muscles marks a significant development in the rehabilitation
technologies field, offering distinct advantages and unique challenges when
compared to conventional rigid systems. These flexible actuators closely
mimic the elasticity of biological tissues, improving safety and interaction
between humans and machines. Designed for individualized therapy, its
versatility allows application in various rehabilitation scenarios, from clinical
settings to home settings. The novelty of this approach lies in the
development of biomechanically-compliant soft pneumatic muscles
optimized for precise rotational control of the elbow joint, coupled with an
advanced deep learning-based motion tracking system. This design overcomes
limitations in force control, stability, and pressure requirements found in existing
pneumatic-based systems, improving the safety and efficacy of elbow
rehabilitation. In this study, the design, fabrication and systematic evaluation
of a soft pneumatic muscle for elbow rehabilitation are presented. The device is
designed to closely simulate the complex biomechanical movements of the
elbow, with a primary focus on the rotational motions that are essential for
controlling flexion and extension, as well as positioning the wrist during grasping
tasks. Through the integration of precise geometric parameters, the actuator is
capable of controlled flexion and extension, reflecting the natural kinematics of
the elbow. Employing a rigorous methodology, the research integrates finite
element analysis with empirical testing to refine the actuator’s performance.
Under varying air pressures, the soft muscle demonstrated remarkable
deformation along the X-axis (10–150 mm) and the Y-axis, indicative of its
symmetrical rotational behavior, while maintaining minimal elongation along
the Z-axis (0.003 mm max), and proper lifiting force under a maximum wight
of 470 gm. highlighting the stability and targeted response of the device to
pneumatic actuation. A specialized experimental apparatus comprising a 3Denvironment, a pneumatic circuit, a LabVIEW-based control system, and a deep
learning algorithm was developed for accurate position estimation. The algorithm
achieved a high predictive accuracy of 99.8% in spatial coordination tracking,
indicating the precision of the system in monitoring and controlling the actuator’s
motion |
