A Wearable Automated System to Quantify Parkinsonian Symptoms Enabling Closed Loop Deep Brain Stimulation

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Paolo Angeles, Imperial College London, London, UK.
  • ,
  • Michael Mace, Imperial College London, London, UK.
  • ,
  • Marcel Admiraal, Imperial College London, London, UK.
  • ,
  • Etienne Burdet, Imperial College London, London, UK.
  • ,
  • Nicola Pavese
  • Ravi Vaidyanathan, Imperial College London, London, UK.

This study presents (1) the design and validation of a wearable sensor suite for the unobtrusive capture of heterogeneous signals indicative of the primary symptoms of Parkinson's disease; tremor, bradykinesia and muscle rigidity in upper extremity movement and (2) a model to characterise these signals as they relate to the symptom severity as addressed by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).

The sensor suite and detection algorithms managed to distinguish between the non-mimicked and mimicked MDS-UPDRS tests on healthy subjects (p

Original languageEnglish
Title of host publicationTOWARDS AUTONOMOUS ROBOTIC SYSTEMS, TAROS 2016
EditorsL Alboul, D Damian, JM Aitken
Number of pages12
PublisherSPRINGER INT PUBLISHING AG
Publication year2016
Pages8-19
ISBN (print)978-3-319-40378-6
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event17th Annual Conference on Towards Autonomous Robotic Systems (TAROS) - Sheffield
Duration: 26 Jun 20161 Jul 2016

Conference

Conference17th Annual Conference on Towards Autonomous Robotic Systems (TAROS)
BySheffield
Periode26/06/201601/07/2016
SeriesLecture Notes in Artificial Intelligence
Volume9716
ISSN0302-9743

    Research areas

  • Parkinson's disease therapy device, Quantification of Parkinson's disease symptoms, Rigidity model, MONITORING-SYSTEM, LIMB BRADYKINESIA, DISEASE, QUANTIFICATION, RIGIDITY, TREMOR

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