This paper analyzes the reliability and usability of a portable electronic instrument that measures balance and balance impairment in older adults. The center of pressure (CoP) metrics are measured with a modified Wii Balance Board (mWBB) platform. In the intra- and inter-rater testing, 16 and 43 volunteers (mean 75.66 and standard deviation (SD) of 7.86 years and 72.61 (SD 7.86) years, respectively) collaborated. Five volunteer raters (5.1 (SD 3.69) years of experience) answered the System Usability Scale (SUS). The most reliable CoP index in the intra-examiner tests was the 95% power frequency in the medial-lateral displacement of the CoP with closed-eyes. It had excellent reliability with an intraclass correlation coefficient ICC = 0.948 (C.I. 0.862–0.982) and a Pearson’s correlation coefficient PCC = 0.966 (p < 0.001). The best index for the inter-rater reliability was the centroidal frequency in the anterior-posterior direction closed-eyes, which had an ICC (2,1) = 0.825. The mWBB also obtained a high usability score. These results support the mWBB as a reliable complementary tool for measuring balance in older adults. Additionally, it does not have the limitations of laboratory-grade systems and clinical screening instruments.

The aim of this study was to improve the diagnostic ability of fall risk classifiers using a Bayesian approach and the Simulated Annealing (SA) algorithm. A total of 47 features from 181 records (40 Center of Pressure (CoP) indices and 7 patient descriptive variables) were analyzed. The wrapper method of feature selection using the SA algorithm was applied to optimize the cost function based on the difference of the mean minus the standard deviation of the Area Under the Curve (AUC) of the fall risk classifiers across multiple dimensions. A stratified 60–20–20% hold-out method was used for train, test, and validation sets, respectively. The results showed that although the highest performance was observed with 31 features (0.815 ± 0.110), lower variability and higher explainability were achieved with only 15 features (0.780 ± 0.055). These findings suggest that the SA algorithm is a valuable tool for feature selection for acceptable fall risk diagnosis. This method offers an alternative or complementary resource in situations where clinical tools are difficult to apply.

Purpose: This study aimed to develop feature extraction strategies for center of pressure (CoP) signals using adaptive genetic programming to characterize fall risk in older adults. Methods: The individual performance of CoP indices reported in the state of the art was optimized through adaptive genetic programming across mathematical domains, such as entropy, time-based (distance, area, hybrid measures), and frequency-based. The validity of the new CoP indices was tested using mean difference tests for groups with and without fall risk, measuring the correlation with existing measures, as well as through the performance of univariate and multiple logistic regressions, which were reported in terms of the macro-average F1-score, recall, accuracy, specificity, sensitivity, and Area Under the Curve (AUC). Results: The newly generated genetic CoP indices outperformed state-of-the-art indices in fall risk identification. The genetic-frequency CoP index achieved the best performance in univariate logistic regression, with an AUC of 0.763 using five-fold cross-validation. Moreover, all genetic indices showed statistically significant differences between older adults with and without fall risk. Conclusions: These results suggest that the proposed methodology provides some simple calculation formulas that facilitate its future adoption in clinical settings and increase fall risk classification performance by up to 27.0%.