TY - JOUR
T1 - TUG test instrumentation for parkinson's disease patients using inertial sensors and dynamic time warping
AU - Adame, M. Reyes
AU - Al-Jawad, A.
AU - Romanovas, M.
AU - Hobert, M. A.
AU - Maetzler, W.
AU - Möller, K.
AU - Manoli, Y.
PY - 2012
Y1 - 2012
N2 - The Timed Up and Go (TUG) test is a clinical tool widely used to evaluate balance and mobility, e.g. in Parkinson's disease (PD). This test includes a sequence of functional activities, namely: sit-to-stand, 3-meters walk, 180° turning, walk back, and turn-to-sit. The work introduces a new method to instrument the TUG test using a wearable inertial sen-sor unit (DynaPort Hybrid, McRoberts B.V., NL) attached on the lower back of the person. It builds on Dynamic Time Warping (DTW) for detection and duration assessment of associated state transitions. An automatic assessment to sub-stitute a manual evaluation with visual observation and a stopwatch is aimed at to gain objective information about the patients. The algorithm was tested on data of 10 healthy individuals and 20 patients with Parkinson's disease (10 pa-tients for early and late disease phases respectively). The algorithm successfully extracted the time information of the sit-to-stand, turn and turn-to-sit transitions. © 2012 by Walter de Gruyter Berlin Boston.
AB - The Timed Up and Go (TUG) test is a clinical tool widely used to evaluate balance and mobility, e.g. in Parkinson's disease (PD). This test includes a sequence of functional activities, namely: sit-to-stand, 3-meters walk, 180° turning, walk back, and turn-to-sit. The work introduces a new method to instrument the TUG test using a wearable inertial sen-sor unit (DynaPort Hybrid, McRoberts B.V., NL) attached on the lower back of the person. It builds on Dynamic Time Warping (DTW) for detection and duration assessment of associated state transitions. An automatic assessment to sub-stitute a manual evaluation with visual observation and a stopwatch is aimed at to gain objective information about the patients. The algorithm was tested on data of 10 healthy individuals and 20 patients with Parkinson's disease (10 pa-tients for early and late disease phases respectively). The algorithm successfully extracted the time information of the sit-to-stand, turn and turn-to-sit transitions. © 2012 by Walter de Gruyter Berlin Boston.
U2 - 10.1515/bmt-2012-4426
DO - 10.1515/bmt-2012-4426
M3 - Zeitschriftenaufsätze
SN - 0013-5585
VL - 57
SP - 1071
EP - 1074
JO - Biomedizinische Technik
JF - Biomedizinische Technik
IS - SUPPL. 1 TRACK-E
ER -