Alzheimer's disease classification based on gait information

Wei Hsin Wang, Yu Liang Hsu, Ming Chyi Pai, Cheng Hsiung Wang, Chun Yao Wang, Chien Wen Lin, Hao Li Wu, Pau Choo Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Alzheimer's disease (AD) is becoming one of the major diseases of the elderly. Traditionally, patients take questionnaires or do some balance tests for clinical evaluation. However, results with such evaluation are subjective. For more objective quantitative measurement, this paper uses an inertial-sensor-based device to measure the gait information while participants walking. In the experiment, the participants are asked to walk on a 40m strike line and take single-task and dual-task tests. In the dual-task test, the participants are asked to count down from 100. This paper presents a stride detection algorithm to automatically acquire gait information of each gait cycle from the acceleration and angular velocity signals. Features are calculated from those inertial signals. After feature generation, we do feature selection to select the significant feature. Then, a probabilistic neural networks (PNNs) is used to classify if the participants suffer from AD. In this paper, we provide an objective way to evaluate the situation of the participants. The experimental results successfully validate the effectiveness of the proposed device and the proposed algorithm with an overall classification accuracy rates are 63.33% and 70.00% in women and men group, respectively.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3251-3257
Number of pages7
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 2014 Sep 3
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 2014 Jul 62014 Jul 11

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period14-07-0614-07-11

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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