Machine Learning-Based Gait Analysis for Distinguishing Older and Younger Walking Patterns in Neurodegenerative Diseases
Keywords:
Gait analysis, neurodegenerative diseases, continuous wavelet transform, machine learning, walking patternsAbstract
Neurodegenerative diseases like Parkinson’s Diseases, Alzheimer’s Diseases, Multiple sclerosis and Huntington’s disease can severe a person’s walking style due to their impact on the brain and the nervous system. Gait analysis, which involves the study of a person's walking pattern and movement, plays a crucial role in the diagnosis and monitoring of these diseases. By examining changes in gait parameters such as stride length, walking speed, and balance, healthcare professionals can gather important information about the underlying neurological impairments and track disease progression. Gait analysis involves the measurement of various parameters, including the stride interval. Changes in the stride interval can indicate alterations in motor control and gait stability, allowing healthcare professionals to assess the severity of neurodegenerative diseases and monitor the effectiveness of treatment interventions. There is lack of research in studying the effect of Continuous Wavelet Transform (CWT) in stride intervals of the young people and old people. It is not clear whether the CWT is a feasible feature extraction method to classify the stride interval of old people and young people. The objective of this paper is to apply Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest algorithms to the maximum Root Mean Square (RMS) value of CWT to determine the most effective machine learning techniques for distinguishing between older and younger walking patterns. KNN stands out the best in performance by scoring 93% for all weighted average (precision), weighted average (recall) and weighted average (f1-score). SVM comes out second in performance by scoring 86% for weighted average (precision), 83% for weighted average (recall) and 84% for weighted average (f1-score) with the shortest processing time, 3.2302s. From the boxplot of the Maximum CWT RMS of the young and the old people, it can be seen that the stride interval of the young people is higher and more diverse than the old people.
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