## 1 Introduction
We believe that the use of data to optimize the development and verification of, and practical integration into, medical services, will be discussed as a benchmark study for the introduction of connected health. Furthermore, the data collected by a sensor for determining the risk of falls has shown academic research value. Previous studies have shown that high specificity and sensitivity are the main objectives of a reliable fall prediction system [18] because the final decision will affect the classification and influence of the intermediate voters in case of several patients and crossovers. In some previous studies [16; 19], inertial sensors were introduced in the community for ensuring community care services, and the TUG features were calculated using a stopwatch and by performing entropy measurements using sensors [9]. These methods are frequently utilized to evaluate physiological data, such as blood pressure, heart rate, and postural stability. Entropy analysis is used for gait evaluation across a spectrum of pathologies [20; 21; 22; 23]. Sample entropy measures the probability that two sequences will remain similar at the next point of a time series. More random signals that are produced by healthy limbs are associated with higher sample entropy values [24]. Healthy human physical conditions are more complex when compared with the pathological movement. This entropy measurement indicates that the low entropy values represent a less random signal produced by an injured limb. A more random signal produced by a healthy condition is associated with a high entropy value. Unlike statistical measures or asymmetry indices, entropy values can provide insight with respect to the presence of what is considered to be desirable variability [24]. The entropy measures for time series, such as sample entropy (SampEn) and approximated entropy (ApEn) [25], can be used to measure the unpredictability (opposite of regularity) of a time series. More recently, [PERSON] et al. [26; 27] proposed multiscale entropy (MSE), a new entropy-based measure for time series that seems to better quantify complexity. These sensors can objectively understand the risk of falling; however, it is difficult to perform subsequent analysis in field experiments because of the large amount of data obtained from different axes of the sensor and because of the implementation of multiple evaluations. Moreover, the sensor does not have a cutoff point for classification; furthermore, there is no comparable experiment group, and it is impossible to extract the association of the fall-risk factors one-by-one. Therefore, when classifying high-dimensional data obtained from devices such as sensors, it is unclear whether the dataset is clearly separable (i.e., whether the interval between similar types is small or the interval between the different types is large). If the dataset is not large (e.g., only a few hundred or thousand data items or only dozens or hundreds of fields per pen), training complex artificial intelligence algorithms may be insufficient. In contrast, small parts of large datasets can be sampled before the operation, and the data can be rapidly understood visually, albeit with limited accuracy, using a simple algorithm with low computational complexity. Recently, [PERSON] et al. [28] used multiscale entropy to analyze the triaxial acceleration signals of the overall TUG and found that segment-based TUG (sTUG) was more irregular and complex for the non-fall-risk subjects compared with the fall risk one's subjects. These results indicate that, compared with the overall TUG, sTUG can reveal more information about postural (transition/gait) stability and provide more predictors of fall risk for assessment among community-dwelling elderly people. Therefore, we attempted to further explore the correlation between the internal sensor and the clinical test as further feedback. We must ensure the correlation of the data with clinical variables.