# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
## 5 Conclusions  
This study demonstrated that a person's chronological age can be estimated using a set of representative gait parameters--such as step width, stride length, and anterior CoM velocity--along with self-reported height and weight. In contrast to prior studies that relied on temporally successive gait images or full time-series signals from IMUs, our approach focused on interpretable biomechanical features, allowing us to draw on a wide range of existing gait research for feature selection and interpretation.  
Using kernel regression combined with a sensitivity-based variable selection method, we achieved a correlation coefficient of 0.78 and a mean absolute error of 9.99 years between the estimated and actual ages. This result may represent an upper bound on the achievable accuracy of age estimation using the proposed approach, especially considering that many previous studies have focused only on classifying individuals into broad age groups. These findings suggest that gait parameters contain sufficient information to enable practical age estimation and may offer a valuable means for long-term monitoring of functional aging.  
The selected explanatory variables included step width, stride length, and anterior velocity, as they differ between different age groups [1, 2, 3, 41, 13, 14, 12, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]. Many of these gait parameters can be estimated using inertial measurement units (IMUs) worn on the body, making the proposed estimation model potentially useful for assessing an individual's health status based on their gait in daily life. However, accurately capturing certain parameters, such as minimum foot clearance, may require multiple IMUs, which could limit the practicality and convenience of the approach. Moreover, the model was trained exclusively on healthy female participants, and its generalizability to other populations--including males, individuals with gait impairments, and diverse ethnic groups--remains to be explored.  
Future work will aim to improve the model's estimation accuracy and test its applicability to broader demographic groups. We also plan to investigate the relationship between the estimated walking age and other indicators of physical function or frailty, to assess the clinical or practical relevance of the proposed method.  
Conceptualization, T.K. and S.O.; methodology, T.K. and S.O.; software, T.K.; validation, T.K. and S.O.; formal analysis, T.K. and S.O.; investigation, T.K. and S.O.; resources, T.K. and Y.A.; writing--original draft preparation, T.K. and S.O.; writing--review and editing, T.K., S.O. and Y.A.; visualization, T.K.; supervision, S.O.; project administration, S.O.; funding acquisition, S.O. All authors have read and agreed to the published version of the manuscript.  
This research was in part funded by The Okawa Foundation for Information and Telecommunications (2025-B-3).  
Not applicable.  
This study utilized a conditionally open database [44], which is not available for redistribution.  
We thank [PERSON] for her assistance in data analysis.  
The authors declare no conflicts of interest.