Literature DB >> 17601199

On the use of low-cost computer peripherals for the assessment of motor dysfunction in Parkinson's disease--quantification of bradykinesia using target tracking tasks.

D P Allen1, J R Playfer, N M Aly, P Duffey, A Heald, S L Smith, D M Halliday.   

Abstract

The potential of computer games peripherals to measure the motor dysfunction in Parkinson's diseases is assessed. Of particular interest is the quantification of bradykinesia. Previous studies used modified or custom haptic interfaces, here an unmodified force feedback joystick and steering wheel are used with a laptop. During testing an on screen cursor moves in response to movements of the peripheral, the user has to track a continuously moving target (pursuit tracking), or move to a predetermined target (step tracking). All tasks use movement in the horizontal axis, allowing use of joystick or steering wheel. Two pursuit tracking tasks are evaluated, pseudo random movement, and a swept frequency task. Two step tracking tasks are evaluated, movement between two or between two of five fixed targets. Thirteen patients and five controls took part on a weekly basis. Patients were assessed for bradykinesia at each session using standard clinical measures. A range of quantitative measures was developed to allow comparison between and within patients and controls using analysis of variance (ANOVA). Both peripherals are capable of discriminating between controls and patients, and between patients with different levels of bradykinesia. Recommendations for test procedures and peripherals are given.

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Year:  2007        PMID: 17601199     DOI: 10.1109/TNSRE.2007.897020

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  10 in total

1.  Quantification of bradykinesia during clinical finger taps using a gyrosensor in patients with Parkinson's disease.

Authors:  Ji-Won Kim; Jae-Ho Lee; Yuri Kwon; Chul-Seung Kim; Gwang-Moon Eom; Seong-Beom Koh; Do-Young Kwon; Kun-Woo Park
Journal:  Med Biol Eng Comput       Date:  2010-10-30       Impact factor: 2.602

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.  Application of modified regression techniques to a quantitative assessment for the motor signs of Parkinson's disease.

Authors:  Bambi R Brewer; Sujata Pradhan; George Carvell; Anthony Delitto
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-10-30       Impact factor: 3.802

4.  PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease.

Authors:  Alexandros T Tzallas; Markos G Tsipouras; Georgios Rigas; Dimitrios G Tsalikakis; Evaggelos C Karvounis; Maria Chondrogiorgi; Fotis Psomadellis; Jorge Cancela; Matteo Pastorino; María Teresa Arredondo Waldmeyer; Spiros Konitsiotis; Dimitrios I Fotiadis
Journal:  Sensors (Basel)       Date:  2014-11-11       Impact factor: 3.576

5.  Perceptual control models of pursuit manual tracking demonstrate individual specificity and parameter consistency.

Authors:  Maximilian G Parker; Sarah F Tyson; Andrew P Weightman; Bruce Abbott; Richard Emsley; Warren Mansell
Journal:  Atten Percept Psychophys       Date:  2017-11       Impact factor: 2.199

6.  An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson's Disease.

Authors:  Giovanni Albani; Claudia Ferraris; Roberto Nerino; Antonio Chimienti; Giuseppe Pettiti; Federico Parisi; Gianluigi Ferrari; Nicola Cau; Veronica Cimolin; Corrado Azzaro; Lorenzo Priano; Alessandro Mauro
Journal:  Sensors (Basel)       Date:  2019-11-02       Impact factor: 3.576

Review 7.  Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms.

Authors:  Anirudha S Chandrabhatla; I Jonathan Pomeraniec; Alexander Ksendzovsky
Journal:  NPJ Digit Med       Date:  2022-03-18

8.  Quantitative Measurement of Akinesia in Parkinson's Disease.

Authors:  Lissette Lalvay; Miguel Lara; Andrea Mora; Fernando Alarcón; Manuel Fraga; Jesús Pancorbo; José Luis Marina; María Ángeles Mena; Jose Luis Lopez Sendón; Justo García de Yébenes
Journal:  Mov Disord Clin Pract       Date:  2016-08-03

9.  What brain signals are suitable for feedback control of deep brain stimulation in Parkinson's disease?

Authors:  Simon Little; Peter Brown
Journal:  Ann N Y Acad Sci       Date:  2012-07-25       Impact factor: 5.691

Review 10.  Technologies Assessing Limb Bradykinesia in Parkinson's Disease.

Authors:  Hasan Hasan; Dilan S Athauda; Thomas Foltynie; Alastair J Noyce
Journal:  J Parkinsons Dis       Date:  2017       Impact factor: 5.568

  10 in total

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