## 1 Introduction  
A variety of gait stability indices have been investigated to identify individuals at a high risk of falling [1; 2]. A common objective of these indices is to facilitate the identification of such individuals, enabling the provision of appropriate interventions to mitigate fall risks [3]. In the pursuit of accessible measures for assessing fall risks, the potential of inertial measurement units (IMUs) for gait analysis has garnered significant interest [4; 5; 6; 7; 8; 9; 10]. For instance, [PERSON] et al. [4] employed an IMU sensor embedded in a shoe to calculate basic gait parameters such as stride and walking speed. [PERSON] et al. [6] distinguished between the maximum Lyapunov exponents of cyclic gait patterns, measured by using IMU sensors attached to the lower thigh, in individuals bearing different loads. [PERSON] et al. [9] developed a prototype of a real-time warning system for falls using an IMU sensor placed inside a jacket. The margin of stability (MoS) [11; 12] is a gait stability index recognized for its strong construct validity. As outlined in Section 2.2, it is derived from the kinematic stability of an inverted pendulum model, representing a simplified walking figure. This index is instrumental in assessing the risk of falling due to postural instability during both normal and perturbed walking in healthy individuals as well as those with pathological gait conditions [13]. Although the MoS can be defined for all directions [14], it is commonly calculated for the anterior and mediolateral directions. Thus far, numerous studies have focused on the MoS [15; 16; 17; 18; 19; 20; 21; 22]. For instance, [PERSON] et al. [15] explored various representative values of the MoS, such as mean and integral values, during a gait cycle among healthy young adults to establish a reference base. [PERSON] et al. [16] found that individuals with Parkinson's disease exhibited higher MoS values than a healthy control group, indicating a difference in walking strategy between groups at high and low fall risks. Such a conservative strategy has typically been observed in the elderly [17; 23; 24]. Additionally, the response of postural stability to unexpected treadmill movements has been analyzed using the MoS [19; 22]. [PERSON] et al. [25] linked the variability of gait parameters with MoS variability, proposing that individuals adjust spatiotemporal gait patterns to preserve postural stability. Thus, the MoS serves as a reliable indicator of postural stability during walking under various conditions. The computation of the MoS necessitates the simultaneous measurement of multiple body points, typically requiring an optical or IMU-based [10] motion capture system, limiting MoS recordings to laboratory settings. However, if the MoS is estimated, using devices such as IMUs embedded in smartphones, this can increase awareness among people about their gait instability and falling risk. Although the computation of the MoS demands positions from multiple body points, the synergy in human motion suggests that measuring fewer points might suffice for estimating the MoS. [PERSON] et al. [26] estimated the mediolateral MoS from the triaxial velocities of the center of body mass in healthy elderly participants, achieving a correlation coefficient of 0.55 between the observed and estimated values. The estimation accuracy can potentially be enhanced by including the angular velocities of body segments [27]. While these studies [26; 27] affirm the feasibility of estimating the MoS using a single IMU, the question of estimate accuracy persists. This concern arises because accurately computing the MoS requires kinematic information from at least three body features: the center of body mass and both feet [11; 12]. Therefore, this study identified conditions that can improve the estimation accuracy, specifically determining the most suitable body points for predicting the MoS. We compared 10 body feature points in terms of the accuracy of the anterior and mediolateral MoS.