Preliminary communication
https://doi.org/10.17559/TV-20160914144554
Analysis of Gait Dynamics of ALS Disease and Classification of Artificial Neural Networks
Omer Akgun
; Marmara University, Department of Computer Engineering, Kuyubasi, Istanbul, 34722, Turkey
Aydin Akan
orcid.org/0000-0001-8894-5794
; Katip Celebi University, Subdiscipline of Biomedical Engineering, Avcilar, Izmir, 35620, Turkey
Hasan Demir
; Namik Kemal University, Electronics & Communications Engineering, Tekirdag, 59000, Turkey
Tahir Cetin Akinci
orcid.org/0000-0002-4657-6617
; Istanbul Technical University, Department of Electrical Engineering, Maslak, Istanbul, 34320, Turkey
Abstract
In this study, a gait device was used for gathering data. A group comprising control group and ALS patients was requested to walk using this device. Gait signals of the control group individuals and ALS patients taken from their left feet were recorded by means of the sensors sensitive to the force which was placed to the device. Spectral and statistical analyses of these signals were made. The results obtained from these analyses were used for making classification with Artificial Neural Network. In consequence of the classification, the individuals with ALS disease were diagnosed accurately with an average rate of 82 %. In the study, the signals taken from left foot of 14 normal individuals and 13 ALS patients were analyzed.
Keywords
ALS Disease; Artificial Neural Nets; Gait Dynamics Analysis; Piezo Electric Sensors; Sound and Vibration
Hrčak ID:
200616
URI
Publication date:
26.5.2018.
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