Machine Learning-Based Predicted Age of the Elderly on the Instrumented Timed Up and Go Test and Six-Minute Walk Test

Abstract

A decrease in dynamic balance ability (DBA) in the elderly is closely associated with aging. Various studies have investigated different methods to quantify the DBA in the elderly through DBA evaluation methods such as the timed up and go test (TUG) and the six-minute walk test (6MWT), applying the G-Walk wearable system. However, these methods have generally been difficult for the elderly to intuitively understand. The goal of this study was thus to generate a regression model based on machine learning (ML) to predict the age of the elderly as a familiar indicator. The model was based on inertial measurement unit (IMU) data as part of the DBA evaluation, and the performance of the model was comparatively analyzed with respect to age prediction based on the IMU data of the TUG test and the 6MWT. The DBA evaluation used the TUG test and the 6MWT performed by 136 elderly participants. When performing the TUG test and the 6MWT, a single IMU was attached to the second lumbar spine of the participant, and the three-dimensional linear acceleration and gyroscope data were collected. The features used in the ML-based regression model included the gait symmetry parameters and the harmonic ratio applied in quantifying the DBA, in addition to the features of description statistics for IMU signals. The feature set was differentiated between the TUG test and the 6MWT, and the performance of the regression model was comparatively analyzed based on the feature sets. The XGBoost algorithm was used to train the regression model. Comparison of the regression model performance according to the TUG test and 6MWT feature sets showed that the performance was best for the model using all features of the TUG test and the 6MWT. This indicated that the evaluation of DBA in the elderly should apply the TUG test and the 6MWT concomitantly for more accurate predictions. The findings in this study provide basic data for the development of a DBA monitoring system for the elderly.

Keywords: XAI; dynamic balance ability; inertial measurement unit (IMU); six-minute walk test; timed up and go test (TUG).

Figure 1

Information of the IMU: (a) definition 3-axis of the IMU; (b) attachment position of the IMU.

Figure 2

Data collection procedure and environment.

Figure 3

The framework for generating the ML-based regression model.

Figure 4

Example of identification of the TUG test sub-tasks (red line: PITCH signal; blue line: YAW signal): (a) rectification for raw data; (b) results of the TUG test data pre-processing for recognizing the TUG test sub-tasks.

Figure 5

Performance comparison of the regression models: (a) MAE comparison results between four algorithms; (b) MAPE comparison results between four algorithms; (c) MAE results of XGBoost models to each datasets; (d) MAPE results of XGBoost models to each datasets.

Figure 6

Analysis of feature importance in each of the XGBoost models as the dataset: (a) key features for each XGBoost model as the dataset; (b) AG; (c) OS; (d) OT.

Figure 6

Analysis of feature importance in each of the XGBoost models as the dataset: (a) key features for each XGBoost model as the dataset; (b) AG; (c) OS; (d) OT.