Paper Info
Title
A Wearable Automated System to Quantify Parkinsonian Symptoms Enabling Closed Loop Deep Brain Stimulation.
Abstract
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 <= 0.15), for all the primary symptoms of Parkinson's disease. Future trials will be conducted on Parkinsonian subjects receiving deep brain stimulation (DBS) therapy. Quantifying symptom severity and correlating severity ratings with DBS treatment will be an important step to fully automate DBS therapy.
Year
DOI
Venue
2016
10.1007/978-3-319-40379-3_2
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
Parkinson's disease therapy device,Quantification of Parkinson's disease symptoms,Rigidity model
Deep brain stimulation,Wearable computer,Rating scale,Physical medicine and rehabilitation,Muscle Rigidity,Parkinsonian Symptoms,Medicine
Conference
Volume
ISSN
Citations 
9716
0302-9743
2
PageRank 
References 
Authors
0.38
5
6
Name
Order
Citations
PageRank
Paolo Angeles120.72
Michael Mace271.36
Marcel Admiraal320.38
Etienne Burdet469593.50
Nicola Pavese552.58
Ravi Vaidyanathan627956.17