Citation

Caldas R, Mundt M, Potthast W, Buarque de Lima Neto F, Markert B. Gait Posture 2017; 57: 204-210.

Affiliation

Institute of General Mechanics, RWTH Aachen University, Germany.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.gaitpost.2017.06.019

PMID

28666178

Abstract

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.

Copyright © 2017 Elsevier B.V. All rights reserved.

Language: en

Keywords

Accelerometer; Artificial intelligence; Gait kinematics; Inertial measurement unit; Machine learning algorithms