title: Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores.
abstract: Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).
authors.affiliation: School of Computing and Communications, University of Technology Sydney, Ultimo, NSW, Australia.
authors.forename: Thuy T
authors.initials: TT
authors.lastname: Pham
authors.affiliation: Human Aerospace Laboratory, Neurology Department, Icahn School of Medicine at Mount Sinai.
authors.forename: Steven T
authors.initials: ST
authors.lastname: Moore
authors.affiliation: Parkinson's Disease ClinicBrain and Mind Research InstituteThe University of Sydney.
authors.forename: Simon John Geoffrey
authors.initials: SJG
authors.lastname: Lewis
authors.affiliation: School of Computing and CommunicationsUniversity of Technology Sydney.
authors.forename: Diep N
authors.initials: DN
authors.lastname: Nguyen
authors.affiliation: School of Computing and CommunicationsUniversity of Technology Sydney.
authors.forename: Eryk
authors.initials: E
authors.lastname: Dutkiewicz
authors.affiliation: Department of PhysiologyUniversity of Arizona.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Fuglevand
authors.affiliation: Department of Electrical and Information EngineeringThe University of Sydney.
authors.forename: Alistair L
authors.initials: AL
authors.lastname: McEwan
authors.affiliation: Department of Electrical and Information EngineeringThe University of Sydney.
authors.forename: Philip H W
authors.initials: PHW
authors.lastname: Leong
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000368:Aged; D000465:Algorithms; D005260:Female; D020233:Gait Disorders, Neurologic / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Accelerometers; Australia; Detectors; Feature extraction; Manuals; Parkinson's disease