Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test.
Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice.
University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.
Danique
D
Vervoort
University Grenoble-Alpes, AGEIS, La Tronche, France.|Institut Universitaire de France, Paris, France.
Nicolas
N
Vuillerme
University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.|University Grenoble-Alpes, AGEIS, La Tronche, France.
Nienke
N
Kosse
University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.
Tibor
T
Hortobágyi
University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.
Claudine J C
CJ
Lamoth
D061725:Accelerometry / Q000145:classification* / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000375:Aging / Q000473:pathology / Q000502:physiology*; D000465:Algorithms; D015374:Biosensing Techniques / Q000145:classification / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D005260:Female; D020233:Gait Disorders, Neurologic / Q000175:diagnosis; D015577:Geriatric Assessment / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement; D015999:Multivariate Analysis; D026741:Physical Therapy Modalities / Q000295:instrumentation / Q000592:standards / Q000706:statistics & numerical data; D004856:Postural Balance / Q000502:physiology; D013997:Time Factors; D055815:Young Adult