## 1 Introduction  
The ability to maintain balance in an upright position during quiet standing and dynamic task conditions is necessary for successful performance in daily life tasks [1; 2]. Human postural control tends to initiate and constraint joint movement so that the center of mass (COM) is positioned over the base of support (BOS) and aligned with the center of pressure (COP), which is known as an equilibrium state. However, any external perturbation that is induced to those parts of the body will result in the shift of COM closer to the BOS border and interrupt alignment between COM and COP, which will cause a non-equilibrium state [3]. However, the balance control systems degenerate as we grow older, and this factor becomes the main reason for the high risk of falls among elderly people. Therefore, a better and deeper understanding of postural control strategies needs to be understood first before the development of training and rehabilitation tools. Depending on task requirements, a human is able to select distinct strategies. Two primary postural control strategies were identified in upright bipeds, which are an ankle strategy and hip strategy [4; 5]. The ankle strategy is viewed as an inverted pendulum with the motion around the ankle joint. Meanwhile, the hip strategy is used when the postural stability creates a particular constraint on the posture when the motion is around the hip joint. With the presence of a hip strategy, [PERSON] and [PERSON] suggested that a single link inverted pendulum will split into a multi-link model [6]. Besides these two primary postural control strategies, an additional strategy mode has also been identified that incorporates the ankle and hip strategies into a coordinated strategy. However, how these strategies are selected to maintain balance is still unknown and needs further investigation. In this study, we only observed the ankle and hip strategies since these are the primary strategies used in postural control. Postural control actions often co-occur with other movements, and such movements may bring about a change from one type of postural coordination to another. This can be caused by the basic patterns of postural coordination being centrally represented by a set of motor programs, and postural transitions being behavioral consequences of changes between programs operating at the level of the central nervous system (CNS) [7; 8]. These, as well as the changes between postural states, are consequences of the self-organized nature of the postural system, which exhibit properties of non-equilibrium phase transitions between attractors [9]. Typically, continuous relative phase (CRP) [10] or point estimate relative phase (PRP) [11] have been applied to investigate the coordination relation of two variables. The in-phase mode (\(\sim\)0\({}^{\circ}\)) indicates that two stationary signals move in the same direction, whereas the anti-phase mode (\(\sim\)180\({}^{\circ}\)) indicates that the signals are moving in the opposite direction. However, until now, there have been no studies that have mentioned the exact frequency at which the postural strategy changes happens. Other methods can also be considered in defining the coordination relation of two variables, such as cross correlation function (CCF) analysis. This analysis is a powerful, efficient, and relatively easy-to-apply tool for quantifying associations between variables. It also can be very useful for investigating spatial and temporal relationships between time-varying signals and can provide further insight into the coordination of movement, muscle activation patterns, and isolation noise within a signal. Furthermore, since the CCF coefficient data shows a resemblance to a sigmoidal pattern, we proposed that the transition frequency could be identified by using the logistic function of the sigmoid model. This is because the sigmoid model provides a good example of non-linear and quickly increasing functions of the probability of disclosure and makes computation easier [12].