title: A Method for Automatic and Objective Scoring of Bradykinesia Using Orientation Sensors and Classification Algorithms.
abstract: Correct assessment of bradykinesia is a key element in the diagnosis and monitoring of Parkinson's disease. Its evaluation is based on a careful assessment of symptoms and it is quantified using rating scales, where the Movement Disorders Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard. Regardless of their importance, the bradykinesia-related items show low agreement between different evaluators. In this study, we design an applicable tool that provides an objective quantification of bradykinesia and that evaluates all characteristics described in the MDS-UPDRS. Twenty-five patients with Parkinson's disease performed three of the five bradykinesia-related items of the MDS-UPDRS. Their movements were assessed by four evaluators and were recorded with a nine degrees-of-freedom sensor. Sensor fusion was employed to obtain a 3-D representation of movements. Based on the resulting signals, a set of features related to the characteristics described in the MDS-UPDRS was defined. Feature selection methods were employed to determine the most important features to quantify bradykinesia. The features selected were used to train support vector machine classifiers to obtain an automatic score of the movements of each patient. The best results were obtained when seven features were included in the classifiers. The classification errors for finger tapping, diadochokinesis and toe tapping were 15-16.5%, 9.3-9.8%, and 18.2-20.2% smaller than the average interrater scoring error, respectively. The introduction of objective scoring in the assessment of bradykinesia might eliminate inconsistencies within evaluators and interrater assessment disagreements and might improve the monitoring of movement disorders.
authors.forename: O
authors.initials: O
authors.lastname: Martinez-Manzanera
authors.forename: E
authors.initials: E
authors.lastname: Roosma
authors.forename: M
authors.initials: M
authors.lastname: Beudel
authors.forename: R W K
authors.initials: RW
authors.lastname: Borgemeester
authors.forename: T
authors.initials: T
authors.lastname: van Laar
authors.forename: N M
authors.initials: NM
authors.lastname: Maurits
mesh_terms: D000368:Aged; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D005385:Fingers / Q000503:physiopathology; D006801:Humans; D018476:Hypokinesia / Q000175:diagnosis* / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology; D000069553:Supervised Machine Learning; D060388:Support Vector Machine