Background: Lower back pain/injuries are common in caregivers, and physical stresses at the lower back during patient care are considered a primary cause. An instrumented hospital bed my help reduce the physical loads during patient repositioning. Objects: We estimated the physical stresses at the lower back during patient repositioning to assess biomechanical benefits of the instrumented hospital bed. Methods: Fourteen individuals repositioned a patient lying on an instrumented hospital bed. Trials were acquired for three types of repositioning (boosting superiorly, pulling laterally, and rolling from supine to side-lying). Trials were also acquired with two bed heights (10 and 30 cm below the anterior superior iliac spine), and with and without the bed tilting feature. During trials, kinematics of an upper body and hand pulling forces were recorded to determine the compressive and shear forces using static equilibrium equations. Repeated measures ANOVA was used to test if the peak compressive and shear forces were associated with repositioning type (3 levels), bed height (2 levels), and bed feature (2 levels). Results: The peak compressive force ranged from 836 N to 3,954 N, and was associated with type (F = 14.661, p < 0.0005) and height (F = 10.044, p = 0.007), but not with bed feature (F = 0.003, p = 0.955). The peak shear force ranged from 66 to 473 N, and was associated with type (F = 8.021, p < 0.005), height (F = 6.548, p = 0.024), and bed feature (F = 22.978, p < 0.0005). Conclusion: The peak compressive force at the lower back during patient repositioning, draws one’s attention as it is, in some trials, close to or greater than the National Institute for Occupational Safety and Health safety criterion (3,400 N). Furthermore, the physical stress decreases by adjusting bed height, but not by using tilting feature of an instrumented bed.

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.