Quantification of Motor Impairment in Parkinson's Disease Using an Instrumented Timed Up and Go Test

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 21; no. 4; pp. 664 - 673
• Main Authors: Palmerini, Luca, Mellone, Sabato, Avanzolini, Guido, Valzania, Franco, Chiari, Lorenzo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Accelerometer; Aged; Algorithms; classification; Data Interpretation, Statistical; Disability Evaluation; Discriminant analysis; Diseases; feature selection; Female; Gait Disorders, Neurologic - etiology; Gait Disorders, Neurologic - physiopathology; Harmonic analysis; Humans; Instruments; Locomotion - physiology; Male; Middle Aged; Movement - physiology; Parkinson Disease - physiopathology; Parkinson's disease; Postural Balance - physiology; Reliability; Reproducibility of Results; Studies; Timed Up and Go (TUG); Turning; Walking - physiology
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2012.2236577

The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson's disease (PD). It consists of rising from a chair, walking, turning, and sitting. Its total duration is the traditional clinical outcome. In this study an instrumented TUG (iTUG) was used to supplement the quantitative information about the TUG performance of PD subjects: a single accelerometer, worn at the lower back, was used to record the acceleration signals during the test and acceleration-derived measures were extracted from the recorded signals. The aim was to select reliable measures to identify and quantify the differences between the motor patterns of healthy and PD subjects; in order to do so, besides comparing each measure individually to find significant group differences, feature selection and classification were used to identify the distinctive motor pattern of PD subjects. A subset of three features (two from Turning, one from the Sit-to-Walk component), combined with an easily-interpretable classifier (Linear Discriminant Analysis), was found to have the best accuracy in discriminating between healthy and early-mild PD subjects. These results suggest that the proposed iTUG can characterize PD motor impairment and, hence, may be used for evaluation, and, prospectively, follow-up, and monitoring of disease progression.