WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson’s Disease and Age-Matched Controls

TL;DR

This paper introduces an open-access dataset of wearable sensor data from Parkinson’s disease patients and controls to study gait and motor symptoms.

Contribution

The novel contribution is the creation of a synchronized, annotated, and clinically rich open-access dataset for Parkinson’s gait analysis.

Findings

The dataset includes IMU and insole data from 100 PD patients and 85 controls.

Data are synchronized with a gait walkway and annotated with video and clinical metadata.

The dataset supports in-clinic and remote assessments of motor symptoms in Parkinson’s disease.

Abstract

Wearable movement sensors are powerful tools for objectively characterizing and quantifying movement. They enhance the precise characterization of gait, balance, and motor symptoms in Parkinson’s disease and related disorders, facilitating in-clinic and remote assessments, disease management, and therapeutic intervention development. Access to high-quality data from these sensors can accelerate discoveries in this clinical population. The WearGait-PD open-access dataset contains raw inertial measurement unit (IMU) and sensorized insole data from 100 individuals with PD and 85 age-matched controls, synchronized to a gait walkway reference system. IMU data include 3-degree of freedom (DOF) acceleration, rotational velocity, magnetic field strength, and orientation for each of 13 sensors on the participant’s body. Sensor insole data include absolute pressure from 16 sensors in each insole and 3-DOF acceleration and rotational velocity. Walkway data include 2D position and relative pressure for each active sensor during every footfall. Frame-by-frame annotation of participant actions during gait and balance tasks was incorporated using synchronized video cameras. All data were associated with demographic information and clinical evaluations (e.g., medications, DBS-status, MDS-UPDRS scores).

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