Citation

Dasgupta P, VanSwearingen J, Sejdić E. Biomed. Eng. Online 2018; 17(1): e122.

Affiliation

Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA. esejdic@ieee.org.

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12938-018-0555-8

PMID

30208897

Abstract

BACKGROUND: There is considerable evidence that a person's gait is affected by cognitive load. Research in this field has implications for understanding the relationship between motor control and neurological conditions in aging and clinical populations. Accordingly, this pilot study evaluates the cognitive load based on gait accelerometry measurements of the walking patterns of ten healthy individuals (18-35 years old).

METHODS: Data points were collected using six triaxial accelerometer sensors and treadmill pressure reports. Stride and window extraction methods were used to process these data points and separate into statistical features. A binary classification was created by using logistic regression, support vector machine, random forest, and learning vector quantization to classify cognitive load vs. no cognitive load.

RESULTS: Within and between subjects, a cognitive load was predicted with accuracy values ranged of 0.93-1 by all four models. Various feature selection methods demonstrated that only 2-20 variables could be used to achieve similar levels of accuracies.

CONCLUSION: Coupling sensors with machine learning algorithms to detect the most minute changes in gait patterns, most of which are too subtle to identify with the human eye, may have a remarkable impact on the potential to detect potential neuromotor illnesses and fall risks. In doing so, we can open a new window to human health and safety prevention.

Language: en

Keywords

Cognitive load; Gait; Gait classification; Learning vector quantization; Logistic regression; Machine learning; Random forest; Stride extraction; Support vector machine