Abstract
The results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to verify fall-risk categorization; and (b) a logistic regression with 100 stepwise iterations is used to divide features into training (80%) and testing sets (20%). The features of (a) and (b) are compared, measuring the similarity of each approach’s decisive features to those of the clinical-test results. In (a), the most significant features are the Y and Z axes, regardless of the segmentation, whereas sTUG outperforms TUG in (b). Comparing the results of (a) and (b) based on the overall TUG test, the Z axis multiscale entropy (MSE) features show significance regardless of the approach: expert opinion or logistic prediction. Among various clinical test combinations, the only commonalities between (a) and (b) are the Y-axis MSE features when walking. Thus, machine learning should be based on both expert domain knowledge and a preliminary analysis with objective screening. Finally, the clinical test results are compared with the inertial sensor results, prompting the proposal for multi-oriented data analysis to objectively verify the sensor results.
Highlights
Falling is a common problem among older people living in the community and can have serious consequences for their lives and the society [1]
We used a clinical test in all timed up and go (TUG) and segment-based TUG (sTUG) tests and considered various cutoff points
We used a logistic regression analysis stepwise 100 times to divide the features into two groups: an 80% training set and 20% test set. (c) We compared the features between (a) and (b) to understand whether the decisive features are similar to the results of the clinical tests
Summary
Falling is a common problem among older people living in the community and can have serious consequences for their lives and the society [1]. Clinical fall-risk assessments typically include questionnaires and functional assessment of the posture, gait, cognition, and other risk factors with respect to falling [3]. These clinical assessments provide an overview of the fall-risk snapshots but are usually subjective, and those who use threshold assessment scores for performing the classification are considered to be decreasing rather than increasing [4]. The TUG test performs a simple, rapid, and applicable clinical assessment of the balance and mobility of older people.
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