Empirical knowledge is lacking on reclined seating postures. To unify such data, a biomechanical model is needed that accurately predicts posture, the relative position of the pelvis, the point of load transfer to the seat, internal and external forces, and the motion paths of the support surfaces. The overall aim of this investigation was, therefore, to create and validate a biomechanical model of reclined seating postures, and to evaluate in vivo measured and predicted data. A two-dimensional biomechanical model was developed, validated and applied. A comprehensive set of biomechanical data was collected from fifteen gender and age diverse subjects to examine the foundational principles for reclined seating ergonomics. The model agreed with 98.8% of measured data on posture across the seated test conditions. There was a significant relationship between modelled and measured force (p < .001, r = .92), which improved after normalisation (p < .001, r = .97) with an 8% full scale error. The model was robust across height and gender. Significant differences in interface pressure (peak pressure, average pressure and area), stature, back muscle activity and spinal curvature were found between all of the seated test postures. Significant relationships were found between the model predictions and all of the experimental data. This research is unique in creating a framework around reclined seating postures which connects previously disparate areas of seating research. The biomechanical model, experimental results, and theories developed from this research have potential implications in research, and design, for applications including backcare chairs, seating for long-term care and patients with neuromotor deficits, wheelchairs and airline seating. Furthermore, this study exists at the interface of anthropometric and biomechanical modelling, and therefore may have cross over potential to digital humans, where their integration with biomechanical models is at the cutting edge of the field.

<p dir="ltr">Measuring human kinematics and kinetics is critical for ergonomists to evaluate ergonomic risks related to physical workloads, which are essential for ensuring workplace health and safety. Human kinematics describes human body postures and movements in 6 degrees of freedom (DOF). In contrast, kinetics describes the external forces acting on the human body, such as the weight of loads being handled. Measuring them in the workplace has remained costly as they require expensive equipment, such as motion capture systems, or are only possible to measure manually, such as measuring the weight through a force gauge. Due to the limitations of existing measurement methods, most ergonomics assessments are conducted in laboratory settings, mainly to evaluate and improve the design of workspaces, production tools, and tasks. Continuous monitoring of workers' ergonomic risks during daily operations has been challenging, yet it is critical for ergonomists to make timely decisions to prevent workplace injuries.</p><p dir="ltr">Motivated by this gap, this dissertation proposed three studies that introduce novel low-cost, minimally intrusive, and automated methods to measure human kinematics and kinetics for ergonomics assessments. Specifically, study 1 proposed ErgoNet, a deep learning and computer vision network that takes a monocular image as input and predicts the absolute 3D human body joint positions and rotations in the camera coordinate system. It achieved a Mean Per Joint Position Error of 10.69 cm and a Mean Per Joint Rotation Error of 13.67 degrees. This study demonstrated the ability to measure 6 DOF joint kinematics for continuous and dynamic ergonomics assessments for biomechanical modeling using just a single camera. </p><p dir="ltr">Studies 2 and 3 showed the potential of using pressure-sensing gloves (i.e., tactile gloves) to predict ergonomics risks in lifting tasks, especially the weight of loads. Study 2 investigated the impacts of different lifting risk factors on the tactile gloves' pressure measurements, demonstrating that the measured pressure significantly correlates with the weight of loads through linear regression analyses. In addition, the lifting height, direction, and hand type were found to significantly impact the measured pressure. However, the results also illustrated that a linear regression model might not be the best solution for using the tactile gloves' data to predict the weight of loads, as the weight of loads could only explain 58 \% of the variance of the measured pressured, according to the R-squared value. Therefore, study 3 proposed using deep learning model techniques, specifically the Convolution Neural Networks, to predict the weight of loads in lifting tasks based on the raw tactile gloves' measurements. The best model in study 3 achieved a mean absolute error of 1.58 kg, representing the most accurate solution for predicting the weight of loads in lifting tasks. </p><p dir="ltr">Overall, the proposed studies introduced novel solutions to measure human kinematics and kinetics. These can significantly reduce the costs needed to conduct ergonomics assessments and assist ergonomists in continuously monitoring or evaluating workers' ergonomics risks in daily operations.</p>