## 1 Introduction  
Fragile X syndrome (FXS) is the leading cause of inherited intellectual disability and autism (frequency estimated at 1/4000-1/7000) [1] caused by pathologic expansions of the CGG repeat polymorphic region of the FMR1 gene. The number of the CGG repeats determines two main mutational categories with different clinical phenotypes--\"premutation\" and \"full mutation\". In the permutation (55-200 CGGs) different phenotypes such as fragile X-associated primary ovarian insufficiency, fragile X-associated tremor ataxia syndrome and fragile X associated neuropsychiatric disorders [2, 3] are caused by the cell toxicity caused by an excessive production of the FMR1 gene. In the full mutation (>200 CGGs) FXS phenotype with symptoms such as intellectual disability, behavioural deficits and global development delays [4] is caused by gene silencing and the loss of protein function. In this context, somatic mosaicism can be a strong FXS phenotype modulator [4]. There are two types of mosaicism--\"size mosaicism\", when both premutation cells and full mutation cells are present, leading to the occurrence of problems connected with both mutational categories; and \"methylation mosaicism\", when full mutation expansions are not completely methylated [5]. In FXS children, musculoskeletal manifestations such as flexible flat feet, joint laxity and hypotonia [6] may lead to non-physiological gait patterns [6; 7]. Gait analysis in FXS children has documented significant alterations such as a characteristic pattern of excessively flexed hip and ankle joints with reduced flexion at the knee, suggesting an overall immature motor control [7; 8]. In terms of surface electromyography, in one study an altered muscle activity was detected, and an asymmetric activation pattern on gastrocnemius lateralis and biceps femoris suggested the presence of compensatory mechanisms actuated by these children to cope with the overall joint laxity and flexible flat feet [7]. In the recent literature, gait patterns associated with various pathologies (i.e., cerebral palsy patients, Parkinsonians, post-stroke patients) have been investigated in the context of gait classification to assist with clinical decision making [9; 10]. Supervised machine learning algorithms operate on a training set of input data (e.g., gait parameters) and known labels of the data (e.g., clinical diagnosis) to train a predictive model to be able to generate accurate predictions for new unlabelled data. [PERSON] et al. in 2007 applied the support vector machine algorithm to distinguish children with cerebral palsy from healthy controls based on kinetic gait parameters, and achieved 97% accuracy [11]. [PERSON] et al. in 2019 used kinematic features as an input for seven different classifiers, and reported the best accuracy for an artificial neural network [12]. [PERSON] et al. in 2016 applied the support vector machine algorithm with gait characteristics obtained from inertial measurement units as an input to classify post-stroke patients, Huntington's disease patients and healthy elderly individuals with an accuracy of 90.5% [13]. [PERSON] et al. in 2012 used gait characteristics and wavelet-based features as an input for support vector machine to classify Parkinsonian patients and healthy controls with 77.3% accuracy. According to the latest review by [PERSON] et al., the application of machine learning to the recognition of gait patterns is likely to provide a potentially realistic diagnosis [9]. However, in the context of FXS, to the best of the authors' knowledge, automatic gait classification has not previously been performed. The aim of the present study was to use gait analysis parameters to classify FXS children from healthy controls and, within FXS children with full mutation, to classify children with mosaicism. Toward this aim, seven supervised machine learning algorithms were applied to a dataset of joint kinematics and surface electromyographic signals collected on twenty FXS children and sixteen controls.