Abstract
In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.
Highlights
Introduction e most important symptom ofParkinson’s disease (PD) is the disturbances in gait that directly affects the daily activities as well as the quality of life [1]. e disturbances in gait characteristics in PD patients are categorized into continuous gait and episodic gait disturbances [2]
It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. e obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living realtime environment
Introduction e most important symptom of Parkinson’s disease (PD) is the disturbances in gait that directly affects the daily activities as well as the quality of life [1]. e disturbances in gait characteristics in PD patients are categorized into continuous gait and episodic gait disturbances [2]
Summary
Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients. Is paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. Aich et al proposed a method that used a wearable accelerometer that can detect FoG, and the validation study was performed that shows a good level of correlation with a correlation coefficient that ranges from 0.961 to 0.984. An algorithmic-based approach has been developed, and it was validated using clinical test and well-known measuring instruments, and a machine learning-based approach has been proposed to detect the PD from the healthy older group using estimated gait characteristics. An algorithmic-based approach has been developed, and it was validated using clinical test and well-known measuring instruments, and a machine learning-based approach has been proposed to detect the PD from the healthy older group using estimated gait characteristics. is system is developed by keeping in mind that it can be used in the home environment as well as in clinical environments
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