Margin of stability (MoS) is a metric used to assess an individual’s dynamic postural stability during walking. It can identify those at risk of falling and enhance their awareness of preventive measures. Although accurately computing the MoS requires capturing the motion of the entire body, previous research has shown that the kinematic information, specifically the three-axial translational velocities of a single bony feature, can estimate the MoS value to some extent. Such information can be obtained from an inertial measurement unit installed in portable devices such as smartphones and smartwatches. Nowadays, it is common for individuals to have two or more such devices. The primary objective of this study is to determine which combination of data from two or three bony features can most accurately predict the MoS. We used a camera-based kinematic database of healthy Japanese walkers, selecting gait data from 30 male and 30 female participants aged 60 and above. The analysis method involved principal motion analysis, a linear predictive model for multi-dimensional time-series data, to predict the MoS, and used cross-validation for stable model assessment. The results exhibited that combining two bony features generally outperform single features, and the combination of the sacral crest and T10 vertebra was the most effective in predicting MoS with an RMSE of 0.0070 m, followed by the combination of the sacral crest and right toe. We did not find any evidence that the combination of three bony features would result in substantially better accuracy than that of two features. This finding suggests that if IMU-equipped devices are placed on these two body parts, they could better assess their risk of falling.

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