Analysis of Center of Pressure Signals by Using Decision Tree and Empirical Mode Decomposition to Predict Falls among Older Adults

Abstract

Falls put older adults at great risk and are related to the body’s sense of balance. This study investigated how to detect the possibility of high fall risk subjects among older adults. The original signal is based on center of pressure (COP) measured using a force plate. The falling group includes 29 subjects who had a history of falls in the year preceding this study or had received high scores on the Short Falls Efficacy Scale (FES). The nonfalling group includes 47 enrollees with no history of falls and who had received low scores on the Short FES. The COP in both the anterior–posterior and mediolateral direction were calculated and analyzed through empirical mode decomposition (EMD) up to six levels. The following five features were extracted and imported to a decision tree algorithm: root-mean-square deviation, median frequency, total frequency power, approximate entropy, and sample entropy. The results showed that there were a larger number of statistically different feature parameters, and a higher classification of accuracy was obtained. With the aid of empirical mode decomposition, the average classification accuracy increased 10% and achieved a level of 99.74% in the training group and 96.77% in the testing group, respectively.

Enhancing age-related postural sway classification using partial least squares-discriminant analysis and hybrid feature set

“…Similarly, time-domain variables, such as sway area and sway velocity in the A-P and M-L directions, presented higher values in the eyes-closed condition than in the eyesopen condition. These findings are consistent with the agerelated changes in CoP reported in the literature [3,53]. The absence of visual control in older and younger people also translated into lower AUC values for PLS-DA models on compliant surfaces but higher values for rigid surfaces.…”

Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning.

“…Of note, these results are comparable with the study of Liao et al(Liao, Wu, Wei, Chou, & Chang, 2021), where they used the same database selected for this article to the analysis of COP signals by using decision tree and empirical mode decomposition to predict falls among older adults. Therefore, this supports ML and COP measures to classify physical activity conditions.…”

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

“…The proportion of high-risk participants or actual falls was mostly lower than 30%, as observed in our study, and it resulted in an imbalanced dataset. Our sample size was larger than those in most of the previous studies [ 18 , 19 , 23 ], but a larger sample size should be aimed to ensure robust modeling for ML in the future. Second, this was a cross-sectional study, and a prospective and follow-up study design would be helpful to determine the predictive validity of these posturographic data.…”

“…[ 11 , 16 , 17 ]. These various ML algorithms can achieve accuracy between 80 and 99.9% [ 11 , 17 , 22 , 23 ], or an area under the curve (AUC) between 85 to 88% according to the receiver’s operating characteristic (ROC) analysis [ 16 ]. The above results support the validity of using posturographic features to classify or predict fall risk and may be superior to personal metrics [ 16 ].…”