BACKGROUND: Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance. RESULTS: Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%. CONCLUSIONS: The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).
BACKGROUND: Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance. RESULTS: Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%. CONCLUSIONS: The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).
Authors: Dylan A Galloway; Stephanie N Blandford; Tangyne Berry; John B Williams; Mark Stefanelli; Michelle Ploughman; Craig S Moore Journal: Glia Date: 2018-12-11 Impact factor: 7.452
Authors: Alice Chen; Megan C Kirkland; Katie P Wadden; Elizabeth M Wallack; Michelle Ploughman Journal: Gait Posture Date: 2020-03-05 Impact factor: 2.840
Authors: Robert W Motl; Jeffrey A Cohen; Ralph Benedict; Glenn Phillips; Nicholas LaRocca; Lynn D Hudson; Richard Rudick Journal: Mult Scler Date: 2017-02-16 Impact factor: 6.312
Authors: Glenn A Phillips; Kathleen W Wyrwich; Shien Guo; Rossella Medori; Arman Altincatal; Linda Wagner; Jacob Elkins Journal: Mult Scler Date: 2014-04-16 Impact factor: 6.312