Mobile robotics has been used in recent years to provide various types of services in fields such as agriculture, surveillance, rehabilitation, space exploration, and logistics, among others. In many cases, mobile robots need to overcome complex obstacles where traditional wheels are not the best solution, and many researchers have proposed legged wheel hybrid designs. This paper presents a comprehensive study on the effect of the geometry of legged wheels on the performance of mobile robots in climbing stairs. The method used to develop this research is dynamic simulation, in which the parameters that affect the kinematics and dynamics of the robot are included. Subsequently, the results of how the robot manages to perform the test, the torque of the motors, and the contact force of the wheels are analyzed. The main hypothesis of this research is that the opening of the legs of the wheels is a geometric parameter that determines whether the structure will be able to climb the stairs. After 63 simulations, the proportional relationship between the diagonal of the stands and the opening of the wheel legs ranges between 1.11 and 1.53. This parameter showed a strong correlation with the torque of the motors and significant differences in terms of the simulations that succeeded in climbing the stairs and those that did not. These results were used to state a design method for flat, robotic structures using legged wheels. This method was validated by an additional simulation that was performed for a four-legged wheel. It can be concluded that the contribution of this work is a series of steps with which to design these mechanical structures to climb the stairs based on the proposed indicator.

This paper presents the design of a novel leg-wheel transformable mechanism. The purpose of this mechanism is to combine the efficiency of wheeled locomotion with the ability of the legs to traverse difficult terrain. The mechanism has two degrees of freedom with decoupled kinematics. The decoupled kinematics of the mechanism allows the rotation and extension/flexion motions to be controlled independently by a pair of actuators, which provides some advantages. In this work, the direct kinematics of the mechanism is solved analytically. On the other hand, due to the complexity of the inverse kinematics, two different numerical methods were evaluated for solving this problem. A model based on neural networks was successfully implemented in a physical prototype to generate the trajectory of a gait cycle in the leg mode of the mechanism.

Over the past decade, noninvasive brain-computer interfaces (BCIs) leveraging electroencephalography(EEG) to decode gait intention have matured from proof-of-concept studies to nearclinicalimplementations. We systematically reviewed 55 studies (January 2015-April 2024) usingPRISMA guidelines, focusing on neurophysiological markers (MRCPs, ERD/ERS, high-γ), signalprocessing pipelines (artifact suppression, time-frequency transforms), machine-learning classifiers(CSP-SVM, ERD SVM), and deep-learning frameworks (spatio-spectral CNNs, LSTM RNNs). Acrossstudies, median classification accuracy rose from 75% (2015-2018) to 87% (2021-2024), while detectionlatency fell below 200ms. Innovations include enhancing intention detection with emotionevokingmusic stimuli (up to early ERD and improved accuracy), decoding pediatric gait kinematicswith state-space models (r = 0.71 hip, 0.59 knee), and session-to-session transfer learning withoutrecalibration (≤4% performance drop). Challenges remain in artifact mitigation, small sample sizes,and limited multi-centre trials. We propose open, standardized datasets, transfer-learning pipelines,and larger clinical validations to accelerate translation.

This paper presents a complete kinematic model of the tibiofemoral joint (TFJ) based on a RRPP + 4-SPS parallel mechanism, where R, P, and S stand for revolute, prismatic, and spherical joints, respectively. The model accounts for the contact between tibia and femur, and the four major ligaments: anterior cruciate, posterior cruciate, medial collateral, and lateral collateral, with anatomical significance in their length variations. An experimental flexion passive motion task is performed, and the kinematic model is tested to determine its capability to reproduce the workspace of the motion task. In addition, an optimization process is performed to simulate prescribed ligament length variations during the motion task. The proposed kinematic model is capable to reproduce with high accuracy an experimental three-dimensional workspace, and at the same time, to simulate prescribed ligament length variation during the spatial flexion task. Prescribed ligament length variations are achieved through an optimization process of the ligament insertion points. This model can be used to improve the multibody kinematic optimization (MKO) process during gait analysis, and also in the design of rehabilitation devices as well as trajectories to accelerate the recovery of injured ligaments. The model shows potential to predict ligament length variations during different motion tasks, and can serve as a basis to develop complex models for kinetostatic and dynamic analyses without dealing with computationally expensive models.