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

1 Citation (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

Fingerprint

Angular velocity
Feature extraction
Neural networks
Sensors
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Wang, W. H., Hsu, Y. L., Pai, M. C., Wang, C. H., Wang, C. Y., Lin, C. W., ... Chung, P. C. (2014). Alzheimer's disease classification based on gait information. In Proceedings of the International Joint Conference on Neural Networks (pp. 3251-3257). [6889762] (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889762
Wang, Wei Hsin ; Hsu, Yu Liang ; Pai, Ming Chyi ; Wang, Cheng Hsiung ; Wang, Chun Yao ; Lin, Chien Wen ; Wu, Hao Li ; Chung, Pau Choo. / Alzheimer's disease classification based on gait information. Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 3251-3257 (Proceedings of the International Joint Conference on Neural Networks).
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title = "Alzheimer's disease classification based on gait information",
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.",
author = "Wang, {Wei Hsin} and Hsu, {Yu Liang} and Pai, {Ming Chyi} and Wang, {Cheng Hsiung} and Wang, {Chun Yao} and Lin, {Chien Wen} and Wu, {Hao Li} and Chung, {Pau Choo}",
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Wang, WH, Hsu, YL, Pai, MC, Wang, CH, Wang, CY, Lin, CW, Wu, HL & Chung, PC 2014, Alzheimer's disease classification based on gait information. in Proceedings of the International Joint Conference on Neural Networks., 6889762, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 3251-3257, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 14-07-06. https://doi.org/10.1109/IJCNN.2014.6889762

Alzheimer's disease classification based on gait information. / Wang, Wei Hsin; Hsu, Yu Liang; Pai, Ming Chyi; Wang, Cheng Hsiung; Wang, Chun Yao; Lin, Chien Wen; Wu, Hao Li; Chung, Pau Choo.

Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3251-3257 6889762 (Proceedings of the International Joint Conference on Neural Networks).

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

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AU - Chung, Pau Choo

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N2 - 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.

AB - 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.

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BT - Proceedings of the International Joint Conference on Neural Networks

PB - Institute of Electrical and Electronics Engineers Inc.

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Wang WH, Hsu YL, Pai MC, Wang CH, Wang CY, Lin CW et al. Alzheimer's disease classification based on gait information. In Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. 2014. p. 3251-3257. 6889762. (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2014.6889762