## 4 Discussion
### Limitations and Future Work  
Although greatly increasing the number of individuals with SRD compared to [PERSON] et al., (2022) [15], the total of eight participants in this study remains far short of the number required to more fully characterize the range of gait patterns associated with the SRD gait. This relatively low number of participants does limit the ability to generalize the findings but does substantially increase the number of participants with SRD who have participated in a laboratory-based study of gait. The current report does provide the first laboratory-based quantitative measures of gait variability in the SRD population. The limited number of participants does prevent answering the question of whether age is associated with either progression or regression of gait in SRD. Thus, future work should continue to expand the number of individuals with SRD participating in laboratory-based gait investigations across all age groups. Additionally, comparisons of gait performance between SRD patients and individuals with other genetic disorders, such as Rett Syndrome, would provide useful information to researchers and clinicians. The application of a variety of linear and non-linear techniques should also be used to characterize overground walking of individuals with SRD, with comparisons made to gait characteristics obtained during treadmill walking. Although treadmill walking offers the advantage of a more controlled environment that allows for the study of the basic gait pattern, fall prevention harnesses offer a degree of protection that is typically not available during overground walking. Therefore, the need for greater dynamic postural control is increased during overground walking, relative to treadmill walking. As overground walking is the 'natural' form of locomotion, with its emphasis on goal-directed behavior, overground walking presents the opportunity to adapt to the environment. However, that very opportunity also creates circumstances that allow extremely variable gait patterns, including stopping, turning, and completely walking off electronic gait mats that are often used to collect data during overground walking. These behaviors result in modified segmental gait motions and associated metrics when compared to the less variable treadmill-driven gait motions. A future manuscript will explore the potential differences between treadmill and overground gaits. Advances in computing power have resulted in the development of powerful analytical techniques that can lead to insights that were previously difficult to uncover. In particular, machine learning approaches have opened new vistas in the understanding of gait control of both neurotypical and patient populations [33]. Identifying relevant gait features by reducing dimensionality and classification, as well as clustering techniques, are just a few of the ways that machine learning has assisted in identifying clinical gait patterns and diagnoses. For a recent review see [PERSON] et al., 2025 [33]. Recently, the use of machine learning has been proposed to create a 'healthy' digital twin for individuals with a disordered gait that provides individualized predictions of joint kinematics during the gait cycle. This procedure serves to help identify stride kinematics that emulate neurotypical patterns versus disordered stride kinematics [34]. The above are just a few of the ways machine learning can be useful to investigators and clinicians attempting to characterize, understand, diagnose, and ultimately improve the gait performance of a variety of populations. As kinematic data sets expand in both the number of participants and number of strides, machine learning is expected to play an ever-increasing role in the analysis of gait patterns produced by individuals with SRD. C.S.L. : conceptualization, investigation and methodology, writing--original draft, visualization; D.M.D. : investigation and methodology, writing--review and editing; C.A.M. : investigation and methodology, writing--review and editing; B.S.