(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r² = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling.

Tibial bone stress injury is a common overuse injury experienced by runners, which results from repetitive tissue forces. Wearable sensor systems (wearables) that monitor tibial forces could help understand and reduce injury incidence. However, there are currently no validated wearables that monitor tibial bone forces. Previous work using simulated wearables demonstrated accurate tibial force estimates by combining a shoe-worn inertial measurement unit (IMU) and pressure insole with a trained algorithm. This study aimed assessed how accurately tibial bone forces could be estimated with existing wearables. Nine recreational runners ran at a series of different speeds and slopes, and with various stride patterns. Shoe-worn IMU and insole data were input into a trained algorithm to estimate peak tibial force. We found an average error of 5.7% in peak tibial force estimates compared with lab-based estimates calculated using motion capture and a force instrumented treadmill. Insole calibration procedures were essential to achieving accurate tibial force estimates. We concluded that a shoe-worn, multi-sensor system is a promising approach to monitoring tibial bone forces in running. This study adds to the literature demonstrating the potential of wearables to monitor musculoskeletal forces, which could positively impact injury prevention, and scientific understanding.

Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool openFoam for automatic design evaluation. For optimization, we chose Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations and has proven to be very sample efficient in a variety of problems, including hyper-parameter tuning and experimental design. During the optimization process, we can handle infeasible design as constraints integrated into the optimization process. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design. For empirical evaluation, we took two different use cases of real-world underwater vehicle design to validate the execution of our tool. The code for running the experimentation and installing the toolchain can be found here https://github.com/vardhah/ConstraintBOUUVHullDesign.