Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

Abstract

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 (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 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.

Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway

“…Secondly, there is a wealth of information residing in the raw gait data that clinicians may not be taking full advantage of. Previous studies focused on the analysis of the predetermined features provided by the conventional software [ 19 ]. In contrast, the present study has shown that it is possible to design and develop new measurements of gait from raw walkway data (toe direction, hull area, BOS area, foot length and foot area).…”

“…Furthermore, data gathered from vertical ground reaction force sensors provided algorithms that detected early signs of Parkinson’s disease [ 18 ]. In the field of MS, there is a study using machine learning techniques to detect which gait parameters were most sensitive to subtle changes in gait [ 19 ]. However, this study and those described above, used the predetermined, and rather limited, gait variables available in conventional proprietary software, meaning clinicians have to interpret what they need from the data.…”

Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach

“…In recent years, machine learning studies have been successfully improved its diagnostic capability in a wide range of medical applications, also being enhanced by support vector machines (SVM) [ 45 , 46 , 47 , 48 ]. Such relatively simple representatives of machine learning algorithms perform quite well when extracting the most relevant information from complex data, providing a more sophisticated approach to classification problems by often mimicking neural networks [ 20 , 49 ].…”

Role of artificial intelligence in MS clinical practice

“…AI is proving to be a promising instrument for the analysis of data measured by these devices. For instance, a combined k-nearest neighbor and support vector machine approach was used to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) for the differentiation between MS patients and HC, and mildly from moderately disabled MS patients ( Trentzsch et al, 2021 ). Furthermore, the analysis of smartphone-derived measures with AI was able to separate MS patients from HC, and mildly from moderately disabled MS patients ( Creagh et al, 2020 ).…”