Literature DB >> 17046261

Long-term monitoring of gait in Parkinson's disease.

Steven T Moore1, Hamish G MacDougall, Jean-Michel Gracies, Helen S Cohen, William G Ondo.   

Abstract

A new system for long-term monitoring of gait in Parkinson's disease (PD) has been developed and validated. The characteristics of every stride taken over 10-h epochs were acquired using a lightweight ankle-mounted sensor array that transmitted data wirelessly to a small pocket PC at a rate of 100 Hz. Stride was calculated from the vertical linear acceleration and pitch angular velocity of the leg with an accuracy of 5 cm. Results from PD patients (5) demonstrate the effectiveness of long-term monitoring of gait in a natural environment. The small, variable stride length characteristic of Parkinsonian gait, and fluctuations of efficacy associated with levodopa therapy, such as delayed onset, wearing off, and the 'off/on' effect, could reliably be detected from long-term changes in stride length.

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Year:  2006        PMID: 17046261     DOI: 10.1016/j.gaitpost.2006.09.011

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  33 in total

1.  Validation of 24-hour ambulatory gait assessment in Parkinson's disease with simultaneous video observation.

Authors:  Steven T Moore; Valentina Dilda; Bandar Hakim; Hamish G Macdougall
Journal:  Biomed Eng Online       Date:  2011-09-21       Impact factor: 2.819

2.  Correlation among joint motions allows classification of Parkinsonian versus normal 3-D reaching.

Authors:  Jacky Chan; Howard Leung; Howard Poizner
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-06-02       Impact factor: 3.802

3.  Estimation of Temporal Gait Events from a Single Accelerometer Through the Scale-Space Filtering Idea.

Authors:  Iván González; Jesús Fontecha; Ramón Hervás; José Bravo
Journal:  J Med Syst       Date:  2016-10-06       Impact factor: 4.460

Review 4.  The relevance of clinical balance assessment tools to differentiate balance deficits.

Authors:  M Mancini; F B Horak
Journal:  Eur J Phys Rehabil Med       Date:  2010-06       Impact factor: 2.874

5.  iTUG, a sensitive and reliable measure of mobility.

Authors:  Arash Salarian; Fay B Horak; Cris Zampieri; Patricia Carlson-Kuhta; John G Nutt; Kamiar Aminian
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2010-04-12       Impact factor: 3.802

6.  Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors.

Authors:  Conrad S Tucker; Ishan Behoora; Harriet Black Nembhard; Mechelle Lewis; Nicholas W Sterling; Xuemei Huang
Journal:  Comput Biol Med       Date:  2015-09-08       Impact factor: 4.589

7.  Validity and repeatability of inertial measurement units for measuring gait parameters.

Authors:  Edward P Washabaugh; Tarun Kalyanaraman; Peter G Adamczyk; Edward S Claflin; Chandramouli Krishnan
Journal:  Gait Posture       Date:  2017-04-12       Impact factor: 2.840

8.  Analyzing 180 degrees turns using an inertial system reveals early signs of progression of Parkinson's disease.

Authors:  Arash Salarian; Cris Zampieri; Fay B Horak; Patricia Carlson-Kuhta; John G Nutt; Kamiar Aminian
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

9.  Locomotor response to levodopa in fluctuating Parkinson's disease.

Authors:  Steven T Moore; Hamish G MacDougall; Jean-Michel Gracies; William G Ondo
Journal:  Exp Brain Res       Date:  2007-09-08       Impact factor: 1.972

10.  Quantifying Parkinson's disease finger-tapping severity by extracting and synthesizing finger motion properties.

Authors:  Yuko Sano; Akihiko Kandori; Keisuke Shima; Yuki Yamaguchi; Toshio Tsuji; Masafumi Noda; Fumiko Higashikawa; Masaru Yokoe; Saburo Sakoda
Journal:  Med Biol Eng Comput       Date:  2016-03-31       Impact factor: 2.602

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