The main aim of this master thesis is modernization of design of electrict vehicle for elderly and disabled. Design should observe all structral, ergonomic and asthetical requierements. The aim is create original design with using new technologies.

Consideration of products' ergonomic qualities is one important component for successful product development. Product designers engaged in the core activity of product development need methods that support the consideration of ergonomics along with other product requirements. This thesis aims to address these needs. The first part of the thesis investigates how people working within product development organisations communicate with and about users of their products. The general need for methods to support communication of user aspects in product development is identified through formal interviews with product developers and a review of the management, ergonomics and design literature. The second part of the thesis studies the factors which affect the integration of ergonomics in product design. Supportive methods, including User Characters, for evoking user consideration among designers together with Overlapping methods for scheduling ergonomics evaluation in product design processes are introduced and argued. The third part of the thesis reviews and discusses computer aided ergonomics as a means for integration of ergonomics in product design. A web-based support system for effective employment of human simulation tools is developed using a participative approach and evaluated based on the system's usability. The objective of the fourth part of the thesis is to study how human simulation tools can aid designers' consideration of human diversity to accommodate users of diverse anthropometric characteristics in multivariate design problems such as automobile cockpits. The work involves the evaluation of different approaches for the generation of specific manikin families which can be used as test groups for fitting trials in the virtual design process. The research demonstrates enhancements in design methodology knowledge to support integration of ergonomics in product design processes with a focus on anthropometric diversity in vehicle design.

<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>