Constructing and Testing a Lightweight Model of Converting Single Stride of In-Shoe-Motion-Sensor-Measured Foot Motion to TUG-Represented Mobility

Mobility is a crucial physical factor that is highly correlated with fall risk and can be modified by physical exercise. The timed up and go (TUG) test outcome is a metric that represents mobility. Monitoring mobility in daily living is essential for fall prevention in older adults. In this study, we aim to develop models for estimating TUG-represented mobility by a single stride of foot motion measured by an in-shoe motion sensor (IMS). In addition, we tested the validity and individual robustness of these models on separately recruited subjects. As a result, we discover novel gait predictors for high-precision estimation from a single stride of foot motion. The models achieved an excellent intraclass correlation coefficient agreement between reference and estimated values with mean absolute errors (MAEs) of 0.39 and 0.41 s (0.34 and 0.40 times the standard deviation) for both male and female subjects. Around 80% of female subjects were effectively estimated within the limit of agreement of Bland–Altman plots, which suggested the individual robustness of the model.