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.

中文翻译：

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基于机器学习的老年人预测年龄的仪器定时起跑测试和六分钟步行测试

老年人动态平衡能力（DBA）的下降与衰老密切相关。各种研究通过应用 G-Walk 可穿戴系统的 DBA 评估方法，例如定时起跑测试 (TUG) 和六分钟步行测试 (6MWT)，研究了量化老年人 DBA 的不同方法。然而，这些方法对于老年人来说通常很难直观地理解。因此，本研究的目标是生成基于机器学习 (ML) 的回归模型，以预测老年人的年龄作为熟悉的指标。该模型基于惯性测量单元（IMU）数据作为DBA评估的一部分，并在基于TUG测试的IMU数据和6MWT的年龄预测方面对模型的性能进行了对比分析。DBA 评估使用了 136 名老年参与者进行的 TUG 测试和 6MWT。在进行 TUG 测试和 6MWT 时，将单个 IMU 连接到参与者的第二腰椎，并收集三维线性加速度和陀螺仪数据。基于 ML 的回归模型中使用的特征包括步态对称参数和用于量化 DBA 的谐波比，以及 IMU 信号的描述统计特征。区分了TUG测试和6MWT的特征集，并基于特征集对回归模型的性能进行了对比分析。XGBoost算法用于训练回归模型。根据 TUG 测试和 6MWT 特征集的回归模型性能比较表明，使用 TUG 测试和 6MWT 的所有特征的模型的性能最好。这表明老年人DBA的评估应同时应用TUG测试和6MWT以获得更准确的预测。本研究结果为开发老年人DBA监测系统提供了基础数据。