Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 17; p. 5930
• Main Authors: Mendoza, Tomas, Lee, Chia-Hsuan, Huang, Chien-Hua, Sun, Tien-Lung
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.09.2021; MDPI
• Subjects: Acceleration; Accidental Falls; Adult; Aged; Balance; Entropy; Falls; features; Gait; Humans; inertial sensor; Mobility; multiscale entropy; permutation entropy; Physicians; Postural Balance; Professionals; Risk assessment; Sensors; short form berg balance scale (SFBBS); Time series; timed up and go (TUG); Walking; features; community-dwelling elderly; short form berg balance scale (SFBBS); inertial sensor; multiscale entropy; random forest; fall risk; permutation entropy; timed up and go (TUG)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21175930

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.