# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
## 1 Introduction  
Human gait patterns vary with age [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. For example, [PERSON] et al. [1] demonstrated that several gait parameters change with age: step width increases, while gait speed and stride length decrease. [PERSON] and [PERSON] [2] reported that the ratios of joint torques and power at the hip, knee, and ankle differed between young and elderly participants, even when walking at the same speed. [PERSON] et al. [3] reviewed prior studies and summarized age-related changes in gait, including step width, stride length, and walking speed. Moreover, strategies for maintaining gait stability during normal walking differ between young and older adults [13, 15]. These findings suggest that a person's age can be estimated based on a combination of gait parameters, similarly to how gait or motion patterns are recognized using combinations of low-level kinematic features [17, 18, 19, 20]. For example, [PERSON] et al. [17] classified the level of proficiency of runners based on multivariate biomechanical variables. Estimating age from observable walking characteristics--referred to as \"walking age\"--has potential applications in healthcare and public services, such as assistive systems for older adults. For instance, knowing the approximate age of pedestrians could inform the planning and deployment of support systems in public facilities, including guidance robots and assistive carts [21; 22]. Additionally, understanding which age group one's gait resembles may help individuals maintain or improve their physical health through self-awareness and lifestyle adjustments. The majority of previous studies can be classified into two categories: classification into age groups and estimation of specific numerical age. Thus far, many studies have addressed age-classification problems [23; 24; 25; 26; 27; 28]. For example, [PERSON] et al. [23] used a support vector machine to classify young and old adults based on gait features extracted from images. [PERSON] et al. [24] proposed a method to separate young and elderly people using their silhouette images. [PERSON] [25] classified the gait characteristics of children and adults. These studies aimed to classify the age of walkers into a few categories and did not estimate the actual age of the walkers. In contrast, the study by [PERSON] et al. [29] estimated the ages of individuals ranging from 2 to 94 years using Gaussian process regression applied to frequency spectra derived from video frames or sequential images. [PERSON] et al. [30] combined classification and regression analyses to estimate the age of walkers. [PERSON] et al. [31] used convolutional neural networks to estimate the gender and age of people based on walking. In some of these studies [23; 24; 29; 30], walking people were monitored using cameras. In contrast, a competition was held to estimate the age and gender of walkers by analyzing their motion data measured by a set of inertial measurement units (IMUs) [32]. Various algorithms were adopted in this competition by different teams; however, methods based on gait parameters were rarely used, and details regarding these methods were not clearly described. Few studies have attempted to predict a person's actual age as a numeric value from their gait characteristics. For example, [PERSON] [25] used step length, stride width, and stride frequency to classify the ages of children (3-5 years) and adults (30-52 years). [PERSON] et al. [23] used minimum foot clearance to classify younger and elderly adults. [PERSON] et al. [27] classified the ages of children and adults using stride length, stride frequency, head length, body length, head-to-body ratio, leg length, and stature. As shown in these studies, gait parameters have been used to classify ages; however, few studies estimate chronological age from gait parameters alone.