Abstract
In this paper, the Multiscale Entropy (MSE) analysis of acceleration data collected from a wearable inertial sensor was compared with other features reported in the literature to observe falling behavior from the acceleration data, and traditional clinical scales to evaluate falling behavior. We use a fall risk assessment over a four-month period to examine >65 year old participants in a community service context using simple clinical tests, including the Short Form Berg Balance Scale (SFBBS), Timed Up and Go test (TUG), and the Short Portable Mental Status Questionnaire (SPMSQ), with wearable accelerometers for the TUG test. We classified participants into fallers and non-fallers to (1) compare the features extracted from the accelerometers and (2) categorize fall risk using statistics from TUG test results. Combined, TUG and SFBBS results revealed defining features were test time, Slope(A) and slope(B) in Sit(A)-to-stand(B), and range(A) and slope(B) in Stand(B)-to-sit(A). Of (1) SPMSQ; (2) TUG and SPMSQ; and (3) BBS and SPMSQ results, only range(A) in Stand(B)-to-sit(A) was a defining feature. From MSE indicators, we found that whether in the X, Y or Z direction, TUG, BBS, and the combined TUG and SFBBS are all distinguishable, showing that MSE can effectively classify participants in these clinical tests using behavioral actions. This study highlights the advantages of body-worn sensors as ordinary and low cost tools available outside the laboratory. The results indicated that MSE analysis of acceleration data can be used as an effective metric to categorize falling behavior of community-dwelling elderly. In addition to clinical application, (1) our approach requires no expert physical therapist, nurse, or doctor for evaluations and (2) fallers can be categorized irrespective of the critical value from clinical tests.
Highlights
Mobility may be an important factor for fear of falling due to significantly high correlations [1].Falling refers to the unexpected change in body position when the center of gravity is out of balance.The body is unable to respond effectively, and falls onto the floor or a lower place [2]
It is hoped that with the two groups of people who are divided based on the existing criteria of the clinical test, we can summarize the t-test to explore whether the groups sorted by the accelerometer feature and Multiscale Entropy (MSE) have the ability to distinguish and sort two groups
From MSE indicators, we found that whether in the X, Y or Z direction, Timed Up and Go (TUG), Berg Balance Scale (BBS), and combined TUG and BSS are all distinguishable, showing MSE can effectively classify participants in these clinical tests using behavioral actions
Summary
Falling refers to the unexpected change in body position when the center of gravity is out of balance. The body is unable to respond effectively, and falls onto the floor or a lower place [2]. This issue has attracted an increased amount of attention as society ages. Aging degrades lower limb function and reduces the ability of the elderly to perform daily activities, even leading to increased probability of falls [3]. The body depends on the balancing mechanism to prevent falls related to inertial force of and on the body. Risk assessment and preventive measures for falls in the elderly will become an important area for developing care of aged populations [5].
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