An HMM-based gait comparison

Using Alzheimer's disease patients as examples

Wei Hsin Wang, Hao Li Wu, Pau-Choo Chung, Ming-Chyi Pai

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

1 Citation (Scopus)

Abstract

The similarity comparisons between single-task walking and dual-task walking on Alzheimer's disease (AD) patients has been commonly performed for cognitive declination measurement. This paper presents a personalized gait similarity measurement approach based on Hidden Markov model for the self-comparison between the single-task walking and dual-task walking. Compared with traditional approaches which use statistics parameters comparison on normal group and AD group, the proposed personalized HMM-based self-comparison approach can avoid the dilemma resulted from personal differences such as walking habits and physical conditions such as height and weight. In this paper, two groups, 42 AD patients and 64 healthy control (HC) people, participate the experiments. The results show the promising of the proposed approach in comparing the AD from the normal people.

Original languageEnglish
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2015-September
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
Publication statusPublished - 2015 Sep 28
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 2015 Jul 122015 Jul 17

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period15-07-1215-07-17

Fingerprint

Hidden Markov models
Statistics
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Wang, W. H., Wu, H. L., Chung, P-C., & Pai, M-C. (2015). An HMM-based gait comparison: Using Alzheimer's disease patients as examples. In 2015 International Joint Conference on Neural Networks, IJCNN 2015 (Vol. 2015-September). [7280795] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280795
Wang, Wei Hsin ; Wu, Hao Li ; Chung, Pau-Choo ; Pai, Ming-Chyi. / An HMM-based gait comparison : Using Alzheimer's disease patients as examples. 2015 International Joint Conference on Neural Networks, IJCNN 2015. Vol. 2015-September Institute of Electrical and Electronics Engineers Inc., 2015.
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title = "An HMM-based gait comparison: Using Alzheimer's disease patients as examples",
abstract = "The similarity comparisons between single-task walking and dual-task walking on Alzheimer's disease (AD) patients has been commonly performed for cognitive declination measurement. This paper presents a personalized gait similarity measurement approach based on Hidden Markov model for the self-comparison between the single-task walking and dual-task walking. Compared with traditional approaches which use statistics parameters comparison on normal group and AD group, the proposed personalized HMM-based self-comparison approach can avoid the dilemma resulted from personal differences such as walking habits and physical conditions such as height and weight. In this paper, two groups, 42 AD patients and 64 healthy control (HC) people, participate the experiments. The results show the promising of the proposed approach in comparing the AD from the normal people.",
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Wang, WH, Wu, HL, Chung, P-C & Pai, M-C 2015, An HMM-based gait comparison: Using Alzheimer's disease patients as examples. in 2015 International Joint Conference on Neural Networks, IJCNN 2015. vol. 2015-September, 7280795, Institute of Electrical and Electronics Engineers Inc., International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, 15-07-12. https://doi.org/10.1109/IJCNN.2015.7280795

An HMM-based gait comparison : Using Alzheimer's disease patients as examples. / Wang, Wei Hsin; Wu, Hao Li; Chung, Pau-Choo; Pai, Ming-Chyi.

2015 International Joint Conference on Neural Networks, IJCNN 2015. Vol. 2015-September Institute of Electrical and Electronics Engineers Inc., 2015. 7280795.

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

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AU - Chung, Pau-Choo

AU - Pai, Ming-Chyi

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N2 - The similarity comparisons between single-task walking and dual-task walking on Alzheimer's disease (AD) patients has been commonly performed for cognitive declination measurement. This paper presents a personalized gait similarity measurement approach based on Hidden Markov model for the self-comparison between the single-task walking and dual-task walking. Compared with traditional approaches which use statistics parameters comparison on normal group and AD group, the proposed personalized HMM-based self-comparison approach can avoid the dilemma resulted from personal differences such as walking habits and physical conditions such as height and weight. In this paper, two groups, 42 AD patients and 64 healthy control (HC) people, participate the experiments. The results show the promising of the proposed approach in comparing the AD from the normal people.

AB - The similarity comparisons between single-task walking and dual-task walking on Alzheimer's disease (AD) patients has been commonly performed for cognitive declination measurement. This paper presents a personalized gait similarity measurement approach based on Hidden Markov model for the self-comparison between the single-task walking and dual-task walking. Compared with traditional approaches which use statistics parameters comparison on normal group and AD group, the proposed personalized HMM-based self-comparison approach can avoid the dilemma resulted from personal differences such as walking habits and physical conditions such as height and weight. In this paper, two groups, 42 AD patients and 64 healthy control (HC) people, participate the experiments. The results show the promising of the proposed approach in comparing the AD from the normal people.

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Wang WH, Wu HL, Chung P-C, Pai M-C. An HMM-based gait comparison: Using Alzheimer's disease patients as examples. In 2015 International Joint Conference on Neural Networks, IJCNN 2015. Vol. 2015-September. Institute of Electrical and Electronics Engineers Inc. 2015. 7280795 https://doi.org/10.1109/IJCNN.2015.7280795