{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:59:20Z","timestamp":1790657960639,"version":"4.1.0"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Early detection of cognitive impairment (CI), including MCI and Alzheimer's disease (AD), remains limited by the invasiveness and cost of current diagnostic tools. While gait analysis is a promising alternative, most studies focus on straight-path walking, which may miss subtle motor-cognitive deficits. We hypothesized that curve walking\u2014especially under dual-task (DT) conditions\u2014would better expose early CI-related changes. Combined with machine learning, this approach offers a low-cost, non-invasive tool to distinguish healthy controls (HC), MCI, and AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Seventy-three older adults (HC, MCI due to Alzheimer's disease, and mild AD) performed curve walking along a 10-m oval path under single-task (ST) and DT conditions (counting backward from 100 by ones) while 3D body joint motion was captured using a Kinect v.2 depth camera. Comprehensive macro- and micro-level gait markers were extracted using signal processing and statistical analysis, followed by gait marker selection and machine learning-based classification of CI vs. HC and AD vs. MCI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 156 extracted gait markers, statistical analysis identified 22 ST and 34 DT markers\u2014including macro, micro-temporal, spatial, and symmetry markers\u2014as selected significantly discriminative markers. CI participants showed slower, less stable gait, with greater variability and asymmetry, particularly under DT conditions. Machine learning models combining ST and DT markers achieved 89.04% accuracy for CI vs. HC and 93.02% for AD vs. MCI, confirming that curved walking under dual-task conditions provides richer discriminative gait information than single-task curved walking alone, and supporting curved dual-task gait analysis as a promising paradigm for early cognitive impairment detection and staging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Curve walking under cognitive load revealed subtle gait deficits linked to CI, enabling accurate, low-cost, and non-invasive detection and staging using depth camera data and machine learning\u2014supporting its potential as a screening approach for supervised clinical and community settings, subject to validation in less-controlled environments.<\/jats:p>\n                  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