Dynamical learning and tracking of tremor and dyskinesia from wearable sensors

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 22, Issue 5, 2014, Pages 982-991

  Cole, Bryan T ^a   Roy, Serge H ^b   De Luca, Carlo J ^a,b,c,d   Nawab, S Hamid ^b,c  

^a MA 02215   (United States)

^b BOSTON UNIVERSITY   (United States)

^c Boston University   (United States)

^d BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Accelerometer signals; Dynamical machine learning; Electromyographic (EMG) signals; Parkinson's disease (PD); Tremor and dyskinesia

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISEASES; HIDDEN MARKOV MODELS; HYBRID SENSORS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; NEURAL NETWORKS; NEURODEGENERATIVE DISEASES; WEARABLE TECHNOLOGY;

ACCELEROMETER SIGNALS; ACTIVITIES OF DAILY LIVING; DYNAMIC NEURAL NETWORKS; DYNAMIC SUPPORTS; ELECTROMYOGRAPHIC; ELECTROMYOGRAPHIC SIGNAL; PARKINSON'S DISEASE; TREMOR AND DYSKINESIA;

WEARABLE SENSORS;

AGED; ALGORITHM; ARTIFICIAL INTELLIGENCE; DYSKINESIA; ELECTROMYOGRAPHY; FEMALE; HUMAN; MALE; MARKOV CHAIN; MIDDLE AGED; MOVEMENT (PHYSIOLOGY); PARKINSON DISEASE; PATHOPHYSIOLOGY; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; SUPPORT VECTOR MACHINE; TREMOR;

AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; DYSKINESIAS; ELECTROMYOGRAPHY; FEMALE; HUMANS; MALE; MARKOV CHAINS; MIDDLE AGED; MOVEMENT; NEURAL NETWORKS, COMPUTER; PARKINSON DISEASE; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINE; TREMOR;

EID: 84923380578     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2014.2310904     Document Type: Article

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