Document Detail

Detecting freezing-of-gait during unscripted and unconstrained activity.
MedLine Citation:
PMID:  22255621     Owner:  NLM     Status:  MEDLINE    
We present a dynamic neural network (DNN) solution for detecting instances of freezing-of-gait (FoG) in Parkinson's disease (PD) patients while they perform unconstrained and unscripted activities. The input features to the DNN are derived from the outputs of three triaxial accelerometer (ACC) sensors and one surface electromyographic (EMG) sensor worn by the PD patient. The ACC sensors are placed on the shin and thigh of one leg and on one of the forearms while the EMG sensor is placed on the shin. Our FoG solution is architecturally distinct from the DNN solutions we have previously designed for detecting dyskinesia or tremor. However, all our DNN solutions utilize the same set of input features from each EMG or ACC sensor worn by the patient. When tested on experimental data from PD patients performing unconstrained and unscripted activities, our FoG detector exhibited 83% sensitivity and 97% specificity on a per-second basis.
Bryan T Cole; Serge H Roy; S Hamid Nawab
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2011     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2011  
Date Detail:
Created Date:  2012-01-18     Completed Date:  2012-06-18     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  5649-52     Citation Subset:  IM    
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MeSH Terms
Actigraphy / methods
Diagnosis, Computer-Assisted / methods*
Electromyography / methods
Gait Disorders, Neurologic / diagnosis,  etiology,  physiopathology*
Monitoring, Ambulatory / methods*
Neural Networks (Computer)
Parkinson Disease / complications,  diagnosis*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Grant Support
5 R01 EB007163-05/EB/NIBIB NIH HHS; 5 R01EB007163-03S1/EB/NIBIB NIH HHS

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