Development of a diagnostic tool for cognitive impairment using a smart navigation device

Shu Hua Tsao, Shau Shiun Jan, Ming Chyi Pai, Ling Hui Chang, Yung Hsiang Cheng, Chun Yu Lin

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

Abstract

Alzheimer's disease (AD) is one of the major conditions suffered by elderly people, one early symptom of which is disorientation. This paper aims to develop and evaluate algorithms to detect the movement patterns of elderly people in order to realize whether they are lost. We then make use of this algorithm to develop a diagnostic tool for cog-nitively impaired elderly people. In this paper, five healthy people are asked to simulate AD subjects in order to build the database with two different labels: (1) normal controls (NCs), and (2) AD subjects. A navigation tool is therefore developed to detect whether the subjects get lost during the well-designed walking navigation experiments. To begin with, we develop this navigation diagnostic tool based on an Android smartphone with a simple graphic user interface, since most elderly people cannot handle too much information at the same time. This diagnostic tool contains four sensors: (1) accelerometer, (2) gyroscope, (3) magnetometer, and (4) global navigation satellite system receiver, in order to calculate the angle of the direction in which the subjects should head. Data on the subjects' acceleration during the experiments is also collected to help analyze the elderly people's cognitive and navigation abilities through signal processing. The raw data collected from the accelerometer is extracted into informative features with a 30-second sliding window. The classification model used to classify whether the subject is an AD patient is the one-nearest-neighbor algorithm. Since the measured behavior is a temporal sequence, an elastic distance measurement, called dynamic time warping distance, is applied for the one-nearest-neighbor algorithm. The experimental results show the effectiveness of this proposed navigation tool, with a classification accuracy of 90% using one-nearest-neighbor algorithm with the mean of z-axis acceleration, and the standard deviation of the resultant acceleration as extracted features.

Original languageEnglish
Title of host publication28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015
PublisherInstitute of Navigation
Pages2501-2510
Number of pages10
ISBN (Electronic)9781510817258
Publication statusPublished - 2015 Jan 1
Event28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015 - Tampa, United States
Duration: 2015 Sep 142015 Sep 18

Publication series

Name28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015
Volume4

Other

Other28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015
CountryUnited States
CityTampa
Period15-09-1415-09-18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Software

Fingerprint Dive into the research topics of 'Development of a diagnostic tool for cognitive impairment using a smart navigation device'. Together they form a unique fingerprint.

Cite this