How do Body Mass Index (BMI) and Gender Affect Time-Up-and-Go Measurements.

Abstract

Time-Up-and-Go (TUG) is a commonly used clinical test to evaluate an individual's gait and frailty state. By combining TUG data with other knowledge, e.g., nutrition and daily habits, informed decisions can be made to delay the progression of or alleviate chronic diseases, such as Parkinson's. Scheduling TUG tests in clinics requires assisted transportation and appointment. With the increasingly overloaded healthcare system, recent advances in e-Health provide an alternative solution. Research studies suggest that it is feasible to perform tests at home and automate gait analysis using intelligent software to classify frailty levels in a remote setting. This allows more frequent monitoring, and clinical appointments are made only to patients at higher risk or those in need. However, conducting the TUG test at home comes with challenges. In this paper, we discuss these challenges, e.g., cluttered environment, and propose solutions. In addition, we investigate whether Body Mass Index (BMI) and gender can affect gait measurement. Our experimental results demonstrate that some machine learning models perform better and the choice of input parameters plays an important role in the classification accuracy. Our experimental results demonstrate that high BMI can be reflected in an individual's TUG, if a robust machine learning model is deployed, while men and women in general show distinct gait measurements. Based on this finding, different thresholds should be defined when making the frail, pre-frail and healthy assessment.