title: Non-invasive detection of the freezing of gait in Parkinson's disease using spectral and wavelet features.
abstract: In this study, we have analyzed the accelerometer data recorded during gait analysis of Parkinson disease patients for detecting freezing of gait (FOG) episodes. The proposed method filters the recordings for noise reduction of the leg movement changes and computes the wavelet coefficients to detect FOG events. Publicly available FOG database was used and the technique was evaluated using receiver operating characteristic (ROC) analysis. Results show a higher performance of the wavelet feature in discrimination of the FOG events from the background activity when compared with the existing technique.
authors.forename: Kimia
authors.initials: K
authors.lastname: Nazarzadeh
authors.forename: Sridhar P
authors.initials: SP
authors.lastname: Arjunan
authors.forename: Dinesh K
authors.initials: DK
authors.lastname: Kumar
authors.forename: Debi Prasad
authors.initials: DP
authors.lastname: Das
mesh_terms: D061725:Accelerometry / Q000379:methods; D000368:Aged; D000465:Algorithms; D016208:Databases, Factual; D005260:Female; D005684:Gait / Q000502:physiology; D020233:Gait Disorders, Neurologic / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007866:Leg / Q000502:physiology; D008297:Male; D008875:Middle Aged; D009038:Motion; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology*; D012372:ROC Curve