# Research on Kinematic Parameters of Multiple Gait Pattern Transitions
## 1 Introduction
Similarly, they analyzed the transition between level and stairs (the transition strides were defined from the left toe off on the present surface to the first left toe off on the future surface) for a greater risk of falling when walking stairs and analyzed three sagittal plane joint angles (left hip, knee, and ankle) and six muscle (biceps femoris, rectus femoris, vastus lateralis, tibialis anterior, gastrocnemius lateralis, and soleus) activity patterns, showing that the transition phases are not only an intermediate before and after the transition but also a distinct prediction of the next stride [17]. However, its definition of the transition stride is based on the movement of one leading lower limb and ignores the consideration of the movement of the other lower limb. In addition, in these studies of gait pattern transition, only the joint angle in the sagittal plane is generally involved, the joint angular velocity and the joint angular acceleration in the sagittal plane are not studied and compared, and, more importantly, the parameters related to the trunk are ignored. However, the joint angular acceleration and the joint angular velocity of these kinematic parameters relative to the joint angle will change earlier; therefore, in order to improve the recognition rate, the research of these kinematic parameters is very necessary. Moreover, through the study of biomechanics of human gait, we found that during the process of walking, the trunk will also swing periodically in the coronal plane [18], and, from the above-mentioned literature [12; 13; 14], we can also see that the movement of the trunk is closely related to walking speed and gait pattern. Thus, we believe that trunk-related parameters have potential applications in gait pattern transition recognition. Therefore, in addition to the study of the kinematic parameters of the leading limb (limb that enters the new gait pattern first) in the transition gait, this article also studied the kinematic parameters of the trailing limb (limb that enters the new gait pattern second) and the upper trunk, including 18 parameters (the sagittal plane angular velocity and the angular acceleration of the hip, knee, and ankle joints of both lower limbs; the resultant velocity and resultant acceleration of the thigh and shank of the leading limb; and the velocity and acceleration of the trunk in the coronal axis direction). In short, compared with the former gait stride before the transition, we predict that there are some kinematic parameters (including the kinematic parameters of the leading limb, the trailing limb, and the trunk in the coronal plane) in the transition stride of various gait pattern transitions that show significant differences, and the differences of these parameters in different phases of the transition stride are not the same. Therefore, this study aims to redefine the transition stride and divide the transition stride into new phases in order to systematically analyze these kinematic parameters, determine which kinematic parameters have significant differences in the transitions (between level and stair walking and between level and ramp walking) of different gait patterns, to determine whether these parameters change differently in different gait pattern transitions, and the order the significant differences occur. In the future, artificial intelligence, neural networks, and machine learning algorithms can be combined to lay the foundation for exoskeleton gait pattern transition recognition research.