Cutoff Point of Mini-Balance Evaluation Systems Test Scores for Elderly Estimated by Center of Pressure Measurements by Linear Regression and Decision Tree Classification

Wen-Yen Liao; Yu-Hsiu Chu; Fan-Yu Liu; Kang-Ming Chang; Li-Wei Chou

doi:10.3390/life12122133

Cutoff Point of Mini-Balance Evaluation Systems Test Scores for Elderly Estimated by Center of Pressure Measurements by Linear Regression and Decision Tree Classification

Life (Basel). 2022 Dec 17;12(12):2133. doi: 10.3390/life12122133.

Authors

Wen-Yen Liao¹, Yu-Hsiu Chu², Fan-Yu Liu³, Kang-Ming Chang^{3

4

5}, Li-Wei Chou^{1

2

6}

Affiliations

¹ Department of Physical Medicine and Rehabilitation, China Medical University Hospital, Taichung 404332, Taiwan.
² Department of Physical Therapy and Graduate Institute of Rehabilitation Science, China Medical University, Taichung 406040, Taiwan.
³ Department of Computer Science and Information Engineering, Asia University, Taichung 413505, Taiwan.
⁴ Department of Digital Media Design, Asia University, Taichung 413505, Taiwan.
⁵ Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 406040, Taiwan.
⁶ Department of Physical Medicine and Rehabilitation, Asia University Hospital, Asia University, Taichung 413505, Taiwan.

Abstract

Background: Understanding balance ability and assessing the risk of possible falls are very important for elderly rehabilitation. The Mini-Balanced Evaluation System Test (Mini-BESTest) is an important survey for older adults to evaluate subject balance, but it is not easy to complete due to various limitations of physical activities, including occasional fear of injury. A center of pressure (CoP) signal can be extracted from a force pressure plate with a short recording time, and it is relatively achievable to ask subjects to stand on a force pressure plate in a clinical environment. The goal of this study is to estimate the cutoff score of Mini-BESTest scores from CoP data.

Methods: CoP signals from a human balance evaluation database with data from 75 people were used. Time domain, frequency domain, and nonlinear domain parameters of 60 s CoP signals were extracted to classify different cutoff point scores for both linear regression and a decision tree algorithm. Classification performances were evaluated by accuracy and area under a receiver operating characteristic curve.

Results: The correlation coefficient between real and estimated Mini-BESTest scores by linear regression is 0.16. Instead of linear regression, binary classification accuracy above or below a cutoff point score was developed to examine the CoP classification performance for Mini-BESTest scores. The decision tree algorithm is superior to regression analysis among scores from 16 to 20. The highest area under the curve is 0.76 at a cutoff point score of 21 for the CoP measurement condition of eyes opened on the foam, and the corresponding classification accuracy is 76.15%.

Conclusions: CoP measurement is a potential tool to estimate corresponding balance and fall survey scores for elderly rehabilitation and is useful for clinical users.

Keywords: Balance Evaluation Systems Test; aging; center of pressure; decision tree; fall; linear regression; rehabilitation.

Grants and funding

This research was funded by China Medical University Hospital (ASIA-110-CMUH-09), China Medical University (CMU-111-ASIA-09), and Asia University Hospital (11151005).