Abstract
Early diagnosis of Parkinson’s diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway—GAITRite; and an accelerometer attached at the lower back—Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 ± 7.81% vs. 80.49 ± 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation.
Highlights
Parkinson’s disease (PD) is a complex neurodegenerative disorder which progresses over time [1]and comprises both motor and non-motor symptoms [2], leading to poor disease management, poorer quality of life [3], and increased health care costs [4]
Gait characteristics were normalized with respect to demographic properties and walking speed to overcome the between participants’ differences within each group (HC and Parkinson’s diseases (PD)) for each walking protocol (CW vs. intermittent walking test (IW))
Both machine learning (ML) models behaved in similar fashion for both walking protocols and gait assessment systems
Summary
Parkinson’s disease (PD) is a complex neurodegenerative disorder which progresses over time [1]and comprises both motor and non-motor symptoms [2], leading to poor disease management, poorer quality of life [3], and increased health care costs [4]. Classification accuracy is inconsistent across studies which may be largely due to methodological differences (e.g., testing protocols, gait assessment systems and normalization of participants’ data) [13,14,17]. Gait assessment systems range from gold standards in the field of gait analysis using camera based motion capture and instrumented walkways [11,12] to wearable devices [22]. Wearable sensors such as accelerometers, gyroscopes and magnetometers [23]
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