# Detection of mild cognitive impairment using various types of gait tests and machine learning

## TL;DR

This study uses gait analysis and machine learning with a Kinect camera to detect early signs of cognitive decline, offering a non-invasive and accessible method for MCI screening.

## Contribution

The study introduces a cost-effective, Kinect-based method combining gait analysis and machine learning for MCI detection.

## Key findings

- Both straight and oval walking patterns provide useful data for MCI detection, with oval paths offering more identifiable gait features.
- The Random Forest model achieved 85.50% accuracy and 83.9% F-score in detecting MCI during oval walking tests.
- The method offers a practical tool for MCI screening in clinical and home settings.

## Abstract

Alzheimer's disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration.

This study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC). Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions.

The study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83.9% F-score in detecting MCI during oval walking tests. This research introduces a cost-effective, Kinect-based method that integrates gait analysis—a key behavioral pattern—with machine learning, offering a practical tool for MCI screening in both clinical and home environments.