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Updated: Jun 10, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

AI-Based Severity Classification of Dementia Using Gait Analysis.

Gangmin Moon¹, Jaesung Cho², Hojin Choi³

¹Department of Rehabilitation Medicine, Hanyang University Guri Hospital, 153, Gyeongchun-ro, Guri-si 11923, Republic of Korea.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

Artificial intelligence (AI) and machine learning (ML) can classify dementia severity using gait analysis. These AI methods effectively handle complex data, improving upon traditional statistical approaches for dementia diagnosis.

Keywords:

artificial intelligence cognitive impairment dementia diagnostic model gait analysis gait parameters machine learning severity classification wearable sensors

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Area of Science:

Computational neuroscience
Biomedical engineering
Artificial intelligence in healthcare

Background:

Dementia severity classification is challenging due to complex, high-dimensional gait data.
Conventional statistical methods may fail to capture subtle patterns in gait analysis.
Artificial intelligence (AI) and machine learning (ML) offer potential solutions for complex data analysis.

Purpose of the Study:

To explore the utility of AI in classifying dementia severity via gait analysis.
To examine how ML addresses limitations of traditional statistical approaches in this domain.
To identify key gait features for dementia severity classification using ML techniques.

Main Methods:

Utilized gait analysis data from 34 mild cognitive impairment (MCI), 25 mild dementia, 26 moderate dementia patients, and 54 healthy controls.
Employed a support vector machine (SVM) classifier for dementia severity categorization.
Applied machine learning techniques, including principal component analysis (PCA) and gradient-based feature selection, for dimensionality reduction and feature identification.

Main Results:

Machine learning effectively identified key gait features relevant to dementia severity.
ML techniques successfully handled high-dimensional gait data, complementing traditional statistical analyses.
AI-based tools demonstrated promise in uncovering subtle gait patterns indicative of dementia severity.

Conclusions:

AI and ML show significant utility in classifying dementia severity using gait analysis data.
ML methods can overcome limitations of conventional statistics in analyzing complex, high-dimensional datasets.
Findings support the development of advanced AI-driven diagnostic models for dementia.