In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

One of the common causes of falls in people with Multiple Sclerosis (pwMS) is walking impairment. Therefore, assessment of gait is of importance in MS. Gait analysis and fall detection can take place in the clinical context using a wide variety of available methods. However, combining these methods while using machine learning algorithms for detecting falls has not been performed. Our objective was to determine the most relevant method for determining fall risk by analyzing eleven different gait data sets with machine learning algorithms. In addition, we examined the most important features of fall detection. A new feature selection ensemble (FS-Ensemble) and four classification models (Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, Support Vector Machine) were used. The FS-Ensemble consisted of four filter methods: Chi-square test, information gain, Minimum Redundancy Maximum Relevance and RelieF. Various thresholds (50%, 25% and 10%) and combination methods (Union, Union 2, Union 3 and Intersection) were examined. Patient-reported outcomes using specialized walking questionnaires such as the 12-item Multiple Sclerosis Walking Scale (MSWS-12) and the Early Mobility Impairment Questionnaire (EMIQ) achieved the best performances with an F1 score of 0.54 for detecting falls. A combination of selected features of MSWS-12 and EMIQ, including the estimation of walking, running and stair climbing ability, the subjective effort as well as necessary concentration and walking fluency during walking, the frequency of stumbling and the indication of avoidance of social activity achieved the best recall of 75%. The Gaussian Naive Bayes was the best classification model for detecting falls with almost all data sets. FS-Ensemble improved the classification models and is an appropriate technique for reducing data sets with a large number of features. Future research on other risk factors, such as fear of falling, could provide further insights.

Postural deformities often manifest themselves in a sagittal imbalance and an asymmetric morphology of the torso. As a novel topographic method, torsobarography assesses the morphology of the back by analysing pressure distribution along the torso in a lying position. At torsobarography’s core is a capacitive pressure sensor array. To evaluate its feasibility as a diagnostic tool, the reproducibility of the system and extracted anatomical associated parameters were evaluated on 40 subjects. Landmarks and reference distances were identified within the pressure images. The examined parameters describe the shape of the spine, various structures of the trunk symmetry, such as the scapulae, and the pelvic posture. The results showed that the localisation of the different structures performs with a good (ICC > 0.75) to excellent (ICC > 0.90) reliability. In particular, parameters for approximating the sagittal spine shape were reliably reproduced (ICC > 0.83). Lower reliability was observed for asymmetry parameters, which can be related to the low variability within the subject group. Nonetheless, the reliability levels of selected parameters are comparable to commercial systems. This study demonstrates the substantial potential of torsobarography at its current stage for reliable posture analysis and may pave the way as an early detection system for postural deformities.

Adolescent idiopathic scoliosis (AIS) is treated with various forms of conservative care or surgery, depending on the degree of severity. When AIS is detected early, it can be monitored and initially treated with reduced invasiveness to prevent further progression. AIS manifests itself through deformations of the trunk, which are mostly identified as asymmetries in manual clinical examination. Torsobarography is a new pressure-based surface topographic system for posture analysis and evaluates such associated morphologic asymmetries. The aim of this study is to investigate the diagnostic validity of torsobarography by examining correlation of extracted torsobarographic indices with the Cobb angle and its ability to differentiate between different severities of scoliosis: no scoliosis, mild scoliosis, and moderate scoliosis. A total of 87 subjects (51 females and 36 males) were examined with torsobarography. Six torsobarographic indices were calculated for all subjects: torsobarography angle (TBA), sagittal imbalance index (SII), torso asymmetry index (TAI), shoulder asymmetry angle (SAA), waist asymmetry angle (WAA), and pelvis asymmetry Angle (PAA). These indices were correlated with the Cobb angle, and the differences between severities were statistically analyzed. Three out of six indices (TBA, TAI, and WAA) were able to significantly distinguish between mild and moderate scoliosis. Additionally, those indices showed moderate correlation ($\rho$ = 0.37–0.50) with the Cobb angle measurements. The WAA was the only statistically significant index capable of differentiating between no scoliosis and moderate scoliosis. This study is the first to demonstrate that torsobarography can distinguish between different severities of scoliosis and thus identify a scoliotic deformity that requires bracing over monitoring.

Adolescent idiopathic scoliosis (AIS) is treated with various forms of conservative care or surgery, depending on the degree of severity. When AIS is detected early, it can be monitored and initially treated with reduced invasiveness to prevent further progression. AIS manifests itself through deformations of the trunk, which are mostly identified as asymmetries in the manual clinical examination. Torsobarography is a new pressure-based surface topographic system for posture analysis and evaluates such associated morphologic asymmetries. The aim of the study is to investigate the diagnostic validity of torsobarography by examining correlation of extracted torsobarographic indices with the Cobb angle and its ability to differentiate between different severities of scoliosis: no scoliosis, mild scoliosis and moderate scoliosis. A total of 87 subjects (51 females, 36 males) were examined with torsobarography. Six torsobarographic indices were calculated for all subjects: torsobarography angle (TBA), sagittal imbalance index (SII), torso asymmetry index (TAI), shoulder asymmetry angle (SAA), waist asymmetry angle (WAA) and pelvis asymmetry Angle (PAA). These indices were correlated with the Cobb angle and the differences between severities were statistically analyzed. Three out of six indices (TBA, TAI, WAA) were able to significantly distinguish between mild and moderate scoliosis. Additionally, those indices showed moderate correlation ("ρ = 0.37–0.50" ) with the Cobb angle measurements. The WAA was the only statistically significant index capable of differentiating between no scoliosis and moderate scoliosis. This study is the first to demonstrate that torsobarography can distinguish between different severities of scoliosis and thus identify a scoliotic deformity that requires bracing over monitoring.