Machine learning differentiation of Parkinson’s disease and normal pressure hydrocephalus using wearable sensors capturing gait impairments

Abstract Gait impairments in patients with Parkinson’s Disease (PD) and Normal Pressure Hydrocephalus (NPH) are diagnosed with visual clinical assessments. Despite standardized gait tests and clinicians’ expertise, such approaches can be subjective and challenging due to similar symptoms between the two diseases. Wearable sensors and machine learning (ML) can assist clinicians by offering objective and quantitative assessments of gait impairments that can help distinguishing between PD and NPH. This study consists of a cohort of 12 PD and 11 NPH patients that performed standardized gait tests. Gait was measured by wearable sensors embedded in patients’ shoes: a three-axis gyroscope, a three-axis accelerometer and eight pressure sensors in each insole. Sensors and computational pipeline to extract gait cycle features were validated and calibrated on 21 healthy subjects. ML approaches were employed to identify changes in gait cycle features between the PD and NPH patients groups. Twenty-seven ML classifiers were compared, leading to select linear support vector machines, resulting in a classification accuracy of 0.70 ± 0.28 and an area under the ROC curve of 0.74 ± 0.39. Combining wearable sensors with ML algorithms trained on gait cycle features from those sensors showed the potential for objective differentiation of gait patterns between PD and NPH patients.