Total hip arthroplasty (THA) is a widely performed procedure to restore hip function in patients with degenerative joint diseases. Traditional “press-fit” fixation methods rely on sufficient bone quality for stability, but additional screw fixation is often necessary for patients with suboptimal bone conditions. However, comprehensive studies utilizing predictive modeling to optimize screw placement strategies in THA remain limited. This study integrates finite element analysis (FEA) with deep learning (DL) to optimize screw fixation strategies, enhancing implant stability and reducing revision rates. The design optimization process was conducted to refine key implant parameters before training the deep learning surrogate model. By utilizing advanced simulation techniques—including Goodness of Fit analysis, Response Graphs, Local Sensitivity Analysis, and Spider Charts—critical factors influencing stress distribution and fixation stability were identified. The optimization process ensured that the dataset used for deep learning training consisted of well-validated simulations, thereby improving the predictive accuracy of stress–strain responses. The findings indicate that optimized screw placement significantly improves load distribution, reducing stress concentrations and enhancing long-term implant stability. The comparative analysis of FEA and DL results showed that the DL-FEA surrogate model successfully replicated deformation patterns, though with a mean squared error (MSE) of 24.06%. While this suggests room for improvement, the model demonstrates potential for streamlining surgical planning. A comparative assessment with traditional methods highlights the advantages of DL-FEA in reducing computational time while maintaining precision. Future improvements will focus on refining the DL model, increasing the dataset size, and incorporating clinical validation. These findings contribute to developing a computational protocol for personalized acetabular cup fixation, with implications for reducing revision rates and improving surgical outcomes.

Robotic gait rehabilitation can increase training intensity and repeatability, yet many systems still rely on fixed assistance or pre-programmed trajectories and provide limited support for functional transitions such as sit-to-stand and stand-to-sit. This study presents RoboREHAB as a system-level integration effort that combines variable-gravity assistance through joint-level torque compensation, a redesigned leg assembly intended to improve knee kinematics, and motion-capture-informed reinforcement learning as a preliminary personalization layer for trajectory tracking. The manuscript defines a unified architecture with explicit module interfaces and signal flow and distinguishes simulation-based evaluation, benchtop prototype evidence, and future validation steps to maintain traceability and align claims with the current level of validation. Simulation-based evaluation under the present configuration indicated reductions in gait trajectory error (66%), balance recovery time (55%), stride deviation (72%), and joint torque variability (66%) relative to a fixed-gravity, predefined-trajectory baseline under matched simulated scenario definitions, together with trajectory tracking within an approximately 5% error margin for the redesigned assembly relative to motion-capture references. Benchtop prototype demonstrations support the subsystem-level feasibility of sensor-driven variable-gravity control and user-adjustable assistance scaling using an embedded control stack. Overall, the present evidence supports the feasibility of the integrated RoboREHAB architecture and defines a staged validation pathway toward future hardware-in-the-loop testing, instrumented full-scale evaluation, and eventual human-subject studies.

Patients who are suffering from neuromuscular disorders or injuries that impair motor control need to undergo rehabilitation to regain mobility. Gait training is commonly prescribed to patients to regain muscle memory. Automated-walking training devices were created to aid this process; while these devices establish accurate ankle-path trajectories, the knee and hip movements are inaccurate. In this work, a redesign of the leg assembly in a multi-purpose rehabilitation robotic device (RoboREHAB) was explored to improve hip- and knee-movement accuracy by adding an extra link and rollers to the assembly. Motion analysis was employed to test feasibility, reinforcement learning was utilized to train the new leg assembly to walk, and the joint motions achieved with the redesign were compared to those achieved by motion-capture (mocap) data. As a key result, the motion analysis showed an improvement in the knee- and hip-path trajectories due to the added roller/joint segment. The redesigned leg assembly, under the reinforcement-learning policy, showed a 5% deviation from the motion-capture joint trajectories with a maximum deviation of 51.177 mm but maintained a similar profile to the mocap trajectory data. This is an improvement over the original two-segment design, which achieved a maximum deviation of 72.084 mm. These results in the knee- and hip-joint movements more closely reflect the mocap and motion-analysis results, validating the redesign and opening it up to further experimentation and technical improvement.