The objective assessment of gait kinematics is crucial in evaluating human movement, informing clinical decisions, and advancing rehabilitation and assistive technologies. Assessing gait symmetry, in particular, holds significant importance in clinical rehabilitation, as it reflects the intricate coordination between nerves and muscles during human walking. In this research, a dataset has been compiled to improve the understanding of gait kinematics. The dataset encompasses motion capture data of the walking patterns of eleven healthy participants who were tasked with completing various activities on a circular path. These activities included normal walking, walking with a weighted dominant hand, walking with a braced dominant leg, and walking with both weight and brace. The walking tasks involving weight and brace were designed to emulate the asymmetry associated with common health conditions, shedding light on irregularities in individuals' walking patterns and reflecting the coordination between nerves and muscles. All tasks were performed at regular and fast speeds, offering valuable insights into upper and lower body kinematics. The dataset comprises raw sensor data, providing information on joint dynamics, angular velocities, and orientation changes during walking, as well as analyzed data, including processed data, Euler angles, and joint kinematics spanning various body segments. This dataset will serve as a valuable resource for researchers, clinicians, and engineers, facilitating the analysis of gait patterns and extracting relevant indices on mobility and balance.