Related Experiment Video
Updated: Jul 30, 2025
Home-Based Monitor for Gait and Activity Analysis
Published on: August 8, 2019
Development of a Gait Feature-Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor
Jeongbin Park1, Hyang Jun Lee1, Ji Sun Park1
1From the PlanB4U Research Institute (J.P., C.H.K., W.J.J., K.W.K.), Seongnam; Department of Neuropsychiatry (H.J.L., J.B.B., J.W.H., K.W.K.), Seoul National University Bundang Hospital, Seongnam; Department of Brain and Cognitive Science (J.S.P., K.W.K.), Seoul National University College of Natural Sciences; Medical Research Collaborating Center (S.W.), Seoul National University Bundang Hospital, Seongnam; and Department of Psychiatry (K.W.K.), Seoul National University, College of Medicine, Korea.
Background And Objectives:
Gait changes are potential markers of cognitive disorders (CDs). We developed a model for classifying older adults with CD from those with normal cognition using gait speed and variability captured from a wearable inertial sensor and compared its diagnostic performance for CD with that of the model using the Mini-Mental State Examination (MMSE).
Methods:
We enrolled community-dwelling older adults with normal gait from the Korean Longitudinal Study on Cognitive Aging and Dementia and measured their gait features using a wearable inertial sensor placed at the center of body mass while they walked on a 14-m long walkway thrice at comfortable paces. We randomly split our entire dataset into the development (80%) and validation (20%) datasets. We developed a model for classifying CD using logistic regression analysis from the development dataset and validated it in the validation dataset. In both datasets, we compared the diagnostic performance of the model with that using the MMSE. We estimated optimal cutoff score of our model using receiver operator characteristic analysis.
Results:
In total, 595 participants were enrolled, of which 101 of them experienced CD. Our model included both gait speed and temporal gait variability and exhibited good diagnostic performance for classifying CD from normal cognition in both the development (area under the receiver operator characteristic curve [AUC] = 0.788, 95% CI 0.748-0.823, p < 0.001) and validation datasets (AUC = 0.811, 95% CI 0.729-0.877, p < 0.001). Our model showed comparable diagnostic performance for CD with that of the model using the MMSE in both the development (difference in AUC = 0.026, standard error [SE] = 0.043, z statistic = 0.610, p = 0.542) and validation datasets (difference in AUC = 0.070, SE = 0.073, z statistic = 0.956, p = 0.330). The optimal cutoff score of the gait-based model was >-1.56.
Discussion:
Our gait-based model using a wearable inertial sensor may be a promising diagnostic marker of CD in older adults.
Classification Of Evidence:
This study provides Class III evidence that gait analysis can accurately distinguish older adults with CDs from healthy controls.
