aceExplainable machine learning for Montreal Cognitive Assessment score estimation from gait in community-dwelling older adults: An exploratory evaluation
Background: Machine learning for gait-based cognitive assessment is rarely benchmarked against a mean-prediction null or checked for explanation stability. We evaluated an explainable multimodal model against this null to estimate Montreal Cognitive Assessment (MoCA) score from gait. We assessed explanation stability and the contribution of joint-angle trajectories. Methods: Fifty community-dwelling older adults walked overground with markerless motion capture, yielding 713 gait cycles. Gait-MoCA associations we…
In 50 community-dwelling older adults, no gait-based or demographics-based model, including the multimodal LSTM, significantly outperformed a null model that simply predicted the training mean MoCA score.
Evidence
- Peer-reviewedClinical Biomechanics2026-10-18
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Truvace Impact Record TRV-2026-1982, v1: “Explainable machine learning for Montreal Cognitive Assessment score estimation from gait in community-dwelling older adults: An exploratory evaluation.” Truvace, 2026-10-20. /record/TRV-2026-1982 (accessed at citation time). sha256 cb4bc31e36cb30d4…
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