Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model’s performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

Molybdenum trioxide (MoO3) is used in various applications including sensors, photocatalysts, and batteries owing to its excellent ionic conductivity and thermal properties. It can also be used as a precursor in the hydrogen reduction process to obtain molybdenum metals. Control of the parameters governing the MoO3 synthesis process is extremely important because the size and shape of MoO3 in the reduction process affect the shape, size, and crystallization of Mo metal. In this study, we fabricated MoO3 nanoparticles using a solution combustion synthesis (SCS) method that utilizes an organic additive, thereby controlling their morphology. The nucleation behavior and particle morphology were confirmed using ultraviolet-visible spectroscopy (UV-vis) and field emission scanning electron microscopy (FE-SEM). The concentration of the precursor (ammonium heptamolybdate tetrahydrate) was adjusted to be 0.1, 0.2, and 0.4 M. Depending on this concentration, different nucleation rates were obtained, thereby resulting in different particle morphologies.