Title: Gait signal classification using an in-house built goniometer and naïve Bayes classifier

Authors: Ruba Khnouf; Enas Abdulhay; Rawan Al Junaidi; Fatima Al Rifai

Addresses: Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan ' Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan ' Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan ' Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

Abstract: This work aims at designing and implementing a knee and an ankle goniometer, both based on potentiometry, and applying the naïve Bayes classifier on the signals obtained from the goniometers to differentiate between male and female gait signals, and to also differentiate between healthy and restricted knee gait signals. Gait signals and other parameters were collected from 60 subjects using the goniometers and WEKA was used to classify this data. The designed goniometers were 97.8% accurate and the naïve Bayes classifier was highly accurate in categorising the signals with an accuracy of at least 86.7%.

Keywords: gait analysis; knee goniometer; ankle goniometer; naive Bayes classifier; biomedical signals; signal analysis; knee angle; gait signal classification; gender recognition; disease diagnosis; low-cost diagnosis; joint disease; gait signals.

DOI: 10.1504/IJMEI.2017.083089

International Journal of Medical Engineering and Informatics, 2017 Vol.9 No.2, pp.134 - 144

Received: 02 Dec 2015
Accepted: 01 May 2016
Published online: 20 Mar 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article