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Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features

Published: 23 August 2016 Publication History

Abstract

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.

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      Published In

      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 8, Issue 1
      January 2017
      363 pages
      ISSN:2157-6904
      EISSN:2157-6912
      DOI:10.1145/2973184
      • Editor:
      • Yu Zheng
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 23 August 2016
      Accepted: 01 January 2016
      Revised: 01 January 2016
      Received: 01 October 2015
      Published in TIST Volume 8, Issue 1

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      Author Tags

      1. Parkinson’s disease
      2. gait harmony
      3. gait pattern recognition
      4. gait phases
      5. gait stability
      6. gait symmetry

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      • Refereed

      Funding Sources

      • Natural Science Foundation of Shaanxi Province
      • National Natural Science Foundation of China
      • National Key Research and Development Program of China
      • Fundamental Research Funds for Central Universities

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      • (2024)Wi-Diag: Robust Multisubject Abnormal Gait Diagnosis With Commodity Wi-FiIEEE Internet of Things Journal10.1109/JIOT.2023.330190811:3(4362-4376)Online publication date: 1-Feb-2024
      • (2023)Human Motion State Recognition Method Based on Plantar Pressure Sensing Technology2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR)10.1109/AIHCIR61661.2023.00086(491-495)Online publication date: 8-Dec-2023
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