Gait motion for naturally curving variously shaped corners

Bilateral Elimination Rule-Based Finite Class Bayesian Inference System for Circular and Linear Walking Prediction

OBJECTIVE

The prediction of upcoming circular walking during linear walking is important for the usability and safety of the interaction between a lower limb assistive device and the wearer. This study aims to build a bilateral elimination rule-based finite class Bayesian inference system (BER-FC-BesIS) with the ability to predict the transition between circular walking and linear walking using inertial measurement units.

METHODS

Bilateral motion data of the human body were used to improve the recognition and prediction accuracy of BER-FC-BesIS.

RESULTS

The mean predicted time of BER-FC-BesIS in predicting the left and right lower limbs' upcoming steady walking activities is 119.32 ± 9.71 ms and 113.75 ± 11.83 ms, respectively. The mean time differences between the predicted time and the real time of BER-FC-BesIS in the left and right lower limbs' prediction are 14.22 ± 3.74 ms and 13.59 ± 4.92 ms, respectively. The prediction accuracy of BER-FC-BesIS is 93.98%.

CONCLUSION

Upcoming steady walking activities (e.g., linear walking and circular walking) can be accurately predicted by BER-FC-BesIS innovatively.

SIGNIFICANCE

This study could be helpful and instructional to improve the lower limb assistive devices' capabilities of walking activity prediction with emphasis on non-linear walking activities in daily living.

Spontaneous gait phase synchronization of human to a wheeled mobile robot with replicating gait-induced upper body oscillating motion

Synchronization between humans is often observed in our daily lives, for example in breathing, in hand clapping in crowds, and in walking. It has been reported that pedestrian gait synchronization maximizes walking flow efficiency. As increasingly more mobile robots are being developed for practical use, it is important to consider how robots may impact pedestrian flows. While there is research on synchronization phenomena between humans and robots, gait synchronization between humans and robots has yet to be studied, particularly synchronization occurring with wheeled humanoid robots while moving. In this paper, we investigated the gait phase synchronization between humans and a wheeled mobile humanoid robot, which moved its upper body in three distinct types of motion patterns: (1) no-motion, (2) arm-swinging (as is common for typical mobile humanoids), and (3) arms-swinging in addition to periodic vertical-oscillation similar to the human upper body movement while walking. Rayleigh test was performed on the distribution of the obtained gait phase differences under each condition and a significant distributional bias was confirmed when participants were walking with the robot that performed both arm-swinging and vertical-oscillation of the upper body. These results suggest that humans can spontaneously synchronize their gaits with wheeled robots that utilize upper body oscillating. These findings can be important for the design of robot-integrated urban transportation systems, such as train stations and airports, where both humans and robots are mobile and a highly efficient flow is required.

Modeling and Simulation of Pedestrian Movement Planning Around Corners

: Owing to the complexity of behavioral dynamics and mechanisms associated with turning maneuvers, capturing pedestrian movements around corners in a mathematical model is a challenging task. In this study, minimum jerk and one-thirds power law concepts, which have been initially applied in neurosciences and brain research domains, were utilized in combination to model pedestrian movement planning around bends. Simulation outputs explained that the proposed model could realistically represent the behavioral characteristics of pedestrians walking through bends. Comparison of modeled trajectories with empirical data demonstrated that the accuracy of the model could further be improved by using appropriate parameters in the one-thirds power law equation. Sensitivity analysis explained that, although the paths were not sensitive to the boundary conditions, speed and acceleration profiles could be remarkably varied depending on boundary conditions. Further, the applicability of the proposed model to estimate trajectories of pedestrians negotiating bends under different entry, intermediate, and exit conditions was also identified. The proposed model can be applied in microscopic simulation platforms, virtual reality, and driving simulator applications to provide realistic and accurate maneuvers around corners.