Validation of a Spatiotemporal Gait Model Using Inertial Measurement Units for Early-Stage Parkinson's Disease Detection During Turns

Objective: Current inertial-based models were mostly limited to gait assessment of straight walking, which may not be efficient for detecting subtle gait disorders at an early stage of Parkinson's disease (PD). As PD patients exhibit more severe gait impairments during turns even before the appearance of gait disorders, gait characteristics during turning can provide promise in the identification of early-stage PD. Methods: We proposed a novel spatiotemporal gait model using inertial measurement units that can assess gait performance in both straight walking and turning. Ten healthy young, ten healthy elderly subjects and ten early-stage PD patients were enrolled in the validation experiment. All participants performed a 7-meter walk test consisting of a straight walking path and turns at a self-selected speed. Spatiotemporal gait parameters from the proposed model were compared with the Vicon motion capture system. Results: A strong correlation of all spatiotemporal parameters (Pearson's R between 0.82 similar to 0.99) between the inertial-based model and the reference was observed. Most measurement differences were within the mean +/- 1.96 standard deviation lines. The absolute bias was below 6.21 ms for all temporal gait parameters, 2.19 cm for stride length and 0.02 m's for walking speed. We show that the proposed model does not only achieve a highly accurate and reliable spatiotemporal gait measurement but also enable the detection of significantly decreased stride length and reduced walking speed in early-stage PD patients at turns compared to the control groups. Significance: Our model offers a potential approach for early-stage PD detection.