# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
## 4 Discussion
[51] reported increased fluctuations in walking velocity and step length with age. Additionally, [PERSON] et al. [52] found that the variability in minimum foot clearance also increases in older populations. The age-estimation error in this study was 9.99 years on average. This value is greater than those of other studies using camera images [29] and motion data for a sustained time of measurement [32]. For the latter approach, the least average estimation error reached 5-7 years, utilizing outputs from multiple IMUs worn by walkers. Hence, a method to improve estimation accuracy is the introduction of the time-series variation of gait parameters into the model. However, in this study, each sample contained a set of gait parameters for a single gait cycle because of the specifications of the database that we used. The temporal variation of gait patterns are also known to pertain to fall risks (e.g., [52,53]), and we plan to expand our study to include continuous multiple gait cycles. Another promising approach is the use of age-class information. [PERSON] et al. [30] classified age into five groups and used an estimation model specified to each group. The same regression model was used for all ages in this study; thus, using different models depending on the age reported by the individual may be beneficial. Some earlier studies classified individuals into broad age groups--such as children, young or middle-aged adults, and older adults [23, 24, 25, 26, 27]--suggesting that a two- or three-level classification may be sufficient for certain applications, such as adaptively planning the deployment of service resources in public facilities. For the purpose of estimating approximate age in large spaces, a mean absolute error of around 10 years may be considered acceptable. In the context of individual healthcare awareness, it could be more meaningful to observe how one's walking age changes over time, such as through continuous physical training or lifestyle improvements. In such cases, precise age estimation may be less critical than tracking relative changes. The limitations and future prospects of this study are as follows. In this study, age was estimated only for healthy women. Gender differences in gait have been reported in previous studies [4, 41, 54, 55, 56]; therefore, when estimating age from gait parameters, different models should be established for men and women. As long as the sexes are treated as distinct groups, the framework proposed in this study is expected to remain applicable. Furthermore, relationships between the walking age considered in this study and physical strength are unclear. If the age of a person is estimated to be lower than their actual age, this does not directly mean that they are in a better fitness condition than others. Future work can investigate the correlation between the walking age of a person and their physical strength. Age estimation based on walking characteristics in this study is generally limited to healthy individuals. Individuals with neuromuscular disorders or arthrosis exhibit gait patterns that differ significantly from those of healthy individuals [57, 58, 59, 60]. Previous studies on walking age estimation have also excluded individuals with such conditions. Estimating the age of people with physical impairments is particularly challenging, as the variations in their gait are primarily driven by the specific nature of their disorders. Therefore, the approach proposed in this study is unlikely to generalize to pathological gait patterns without substantial modification, as such patterns are shaped more by the underlying condition than by chronological age. Another challenge in applying the proposed method to practical, everyday use is accounting for footwear conditions. Footwear has been shown to influence gait patterns [61], and it remains uncertain whether a single model can adequately handle gait data collected under both barefoot and shed conditions.