Gait classification of patients with Fabrys disease based on normalized gait features obtained using multiple regression models Diagnosis of Fabry disease (FD) remains a challenge mostly due to its rare occurrence and phenotipical variability, with considerable delay between onset and clinical diagnosis. It is then of extreme importance to explore biomarkers capable of assisting the earlier diagnosis of FD. There is growing evidence supporting the use of gait assessment in the diagnosis and management of several neurological diseases. In fact, gait abnormalities have previously been observed in FD, justifying further investigation. The aim of this study is to evaluate the effectiveness of different machine learning strategies when distinguishing patients with FD from healthy controls based on normalized gait features. Gait features of an individual are affected by physical characteristics including age, height, weight, and gender, as well as walking speed or stride length. Therefore, in order to reduce bias due to intersubject variations a multiple regression (MR) normalization approach for gait data was performed. Four different machine learning strategies  Support Vector Machines (SVM), Random Forest (RF), Multiple Layer Perceptrons (MLPs), and Deep Belief Networks (DBNs)  were employed on raw and normalized gait data. Wearable sensors positioned on both feet were used to acquire the gait data from 36 patients with FD and 34 healthy subjects. Gait normalization using MR revealed significant differences in percentage of stance phase spent in foot flat and pushing ( p  ), with FD presenting lower percentages in foot flat and higher in pushing. No significant differences were observed before gait normalization. Support Vector Machine was the superior classifier achieving an FD classification accuracy of 78.21% after gait normalization, compared to 71.96% using raw gait data. Gait normalization improved the performance of all classifiers. To the best of our knowledge, this is the first study on gait classification that includes patients with FD, and our results support the use of gait assessment on the clinical assessment of FD.

Different Performances of Speech and Natural Gait in Identifying Anxiety and Depression. Recognition of individualxe2x80x99s emotions using the data of speech and natural gait has been proved effective by previous studies. This study establishes a supervised regression model based on individualxe2x80x99s speech and natural gait data to predict the scores of depression and anxiety. The results show that the performance of the predictive model based on gait features is better than that of speech features. The basic processes are as follows: First, we recruited 88 participants and collected their speech data and natural gait data in laboratory; Second, we required each participant to finish the anxiety and depression scales (PHQ9, CESD, TAI, and GAD7), to get the anxiety and depression scores as the tagging data for modeling; Third, we extracted 6125 features from each participantxe2x80x99s speech data and 1602 features from the gait data; Finally, we used a variety of machine learning algorithms to train and test the predictive models. The results show that the max Pearson Correlation Coefficient between the predicted scores and the questionnaire scores of the speechbased model is 0.43, and the max Pearson Correlation Coefficient of the gaitbased model is 0.73, which is much better than the speechbased model.