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.

The authors have declared no competing interest.

The research work in the MoveSenseAI project was supported by the Luxembourg National Research Fund (Fonds National de la Recherche, FNR BRIDGES 2020/BM/14772888) and the IEE S.A. company in Luxembourg. Note that all funding bodies played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The clinical study contained in this work was conducted at the Centre Hospitalier de Luxembourg according to the principles of the Declaration of Helsinki (2013). The Ministry of Health of Luxembourg and the National Ethics Board (Comite National d'Ethique de Recherche, CNER) gave ethical approval for the clinical study contained in this work (Reference number: 202101/02, 0622-2). The validation study contained in this work was carried out on healthy subjects at the Trier University of Applied Sciences according to the principles of the Declaration of Helsinki (2013). The Ethics committee of the State Chamber of Medicine in Rhineland-Palatinate, Germany, gave ethical approval for the validation study contained in this work (Reference name: VitalMove Study).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Raw sensor data were collected at the Centre Hospitalier de Luxembourg (CHL). The data for this manuscript is not publicly accessible as it is subject to CHL and its internal regulations. For reasons of patient confidentiality and privacy, anonymised summary data that support the findings of this study are available from the corresponding author or the first authors upon reasonable request and with permission. All computational codes for this project and preprocessed anonymised data necessary to reproduce all results will be made publicly available upon publication on the GitHub repository: https://github.com/StefanoMagni/MoveSenseAI.git.

https://github.com/StefanoMagni/MoveSenseAI.git