Respirable crystalline silica (RCS) overexposure can lead to the development of silicosis which is a chronic, irreversible, potentially fatal respiratory disease. The most significant prerequisite for any silica exposure control plan is an accurate occupational exposure assessment. The results of crystalline silica analysis are often affected by other mineral interferences and are influenced by an analyst’s knowledge of mineralogy to accurately interpret infrared spectra and correct matrix interferences. Partial least squares (PLS) and artificial neural networks (ANNs) are two multivariate calibration methods to overcome the problem of spectral interferences without the need for an analyst intervention. The performance of these two methods in quantitative analysis of quartz in the presence of mineral interferences was evaluated and compared in this study. Fifty mixtures with different crystalline silica content ratios were prepared by mixing quartz with four common mineral interferences including kaolinite, albite, muscovite, and amorphous silica. Fourier-transform infrared spectra of the mixtures were split into training and test datasets. The optimal architecture of the ANN model was achieved using a two-level full factorial design experiment and data were modeled using ANN and PLS regression analysis. Root mean squared error of prediction values of 1.69 and 6.12 µg quartz for ANN and PLS models, respectively, revealed the fact that the both models performed very well in quantitative analysis of quartz in the presence of mineral interferences, with a better relative performance of the ANN model which can be related to the inherent nonlinear predictive ability of ANNs. Given the excellent predictive ability of the ANN model which can deal with a completely overlapped peak without any need of user’s intervention, it is recommended that the ANN model be optimized in future studies and utilized for reliable and rapid on-field assessment of RCS exposure.

Purpose. Recently, a new method was proposed for physical work rate classification based on an adaptive neuro-fuzzy inference system (ANFIS). This study aims to present a genetic algorithm (GA)-optimized ANFIS model for a highly accurate classification of physical work rate. Methods. Thirty healthy men participated in this study. Directly measured heart rate and oxygen consumption of the participants in the laboratory were used for training the ANFIS classifier model in MATLAB version 8.0.0 using a hybrid algorithm. A similar process was done using the GA as an optimization technique. Results. The accuracy, sensitivity and specificity of the ANFIS classifier model were increased successfully. The mean accuracy of the model was increased from 92.95 to 97.92%. Also, the calculated root mean square error of the model was reduced from 5.4186 to 3.1882. The maximum estimation error of the optimized ANFIS during the network testing process was ± 5%. Conclusion. The GA can be effectively used for ANFIS optimization and leads to an accurate classification of physical work rate. In addition to high accuracy, simple implementation and inter-individual variability consideration are two other advantages of the presented model.

Recent advances in human pose estimation (HPE) have enabled markerless motion capture (MoCap) techniques as a promising alternative to traditional marker-based MoCap systems. However, most HPE algorithms only provide sparse video keypoints, which are insufficient to estimate joint angles in all anatomical planes according to biomechanical guidelines. OpenCap, an open-source smartphone-based markerless MoCap platform, addresses this limitation using a deep learning model (named the marker augmenter) that predicts dense anatomical markers from sparse video keypoints. However, it has shown lower performance for activities not included in its training dataset, such as occupational lifting tasks. In this study, we adapted the original marker augmentation model of OpenCap and proposed a task-specific model for occupational lifting, trained on a large and diverse dataset of manual lifting tasks. The proposed model reduced both kinematic errors (mean RMSE = 9.45° vs. 15.04°) and error variability (SD = 7.26° vs. 16.13°) compared to the original model. These findings suggest that OpenCap can be adapted for occupational lifting tasks, offering a low-cost, easy-to-use, and field-viable solution to collect 3D lifting kinematics for ergonomics applications.

This study evaluated the impacts of five support levels (40, 55, 70, 85, and 100 % actuator strengths) of a passive back-support exoskeleton (BSE) on biomechanical loads associated with commercial crab sorting through musculoskeletal simulation. Whole-body kinematics of 20 male participants performing simulated crab sorting were collected and integrated into a human-BSE interaction simulation framework to predict muscle activity, spinal loads, and contact forces at the human-BSE interfaces. Increasing the BSE support level generally reduced trunk extensor muscle activity (up to 28.6 %) and lumbosacral reaction forces (up to 30.7 %). However, the reduced biomechanical load came at the expense of increased contact forces on the chest and thigh areas, potentially causing local discomfort for vulnerable users. These findings suggest that while BSEs can be effective in reducing low back biomechanical loads during crab sorting, support levels should be carefully selected to ensure adequate assistance while minimizing potential side effects, such as local discomfort or pain.

This study evaluated the effects of passive back-support exoskeletons (BSEs) on biomechanical load and postural stability during simulated manual timber felling at three cutting heights. Twenty healthy adult male participants performed timber felling tasks under four BSE conditions (two rigid, one soft, and no BSE). During the tasks, electromyographic (EMG) activity, trunk angle, postural sway, and perceived exertion were measured. Results showed that rigid BSEs reduced trunk flexion and low back EMG activity at the middle cutting height, where trunk flexion was greatest. No significant changes were observed at low or high cutting heights, likely due to insufficient exoskeleton engagement. BSE use shifted postural control strategies rather than providing a uniform stabilizing effect. Additionally, BSEs lowered perceived exertion for the whole body and low back. These findings suggest that BSEs may help reduce low back loading at the middle cutting height during manual timber felling, but further field testing is warranted.