title: Detecting freezing-of-gait during unscripted and unconstrained activity.
abstract: 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.
authors.affiliation: Dept of Electrical and Computer Engineering,Boston University, Boston, MA 02215, USA. bcole@bu.edu
authors.forename: Bryan T
authors.initials: BT
authors.lastname: Cole
authors.forename: Serge H
authors.initials: SH
authors.lastname: Roy
authors.forename: S Hamid
authors.initials: SH
authors.lastname: Nawab
mesh_terms: D056044:Actigraphy / Q000379:methods; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004576:Electromyography / Q000379:methods; D005684:Gait; D020233:Gait Disorders, Neurologic / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D016571:Neural Networks, Computer; D010300:Parkinson Disease / Q000150:complications / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity