Work-related musculoskeletal disorders (WRMSDs) are a leading cause of work leave in the construc tion industry. As Strukton experiences a shortage in workers for rail catenary construction, minimizing the occurance of WRMSDs is important. This is done by the implementation of new working methods or new worker aids, such as boom lifts and exoskeletons. Strukton has experienced that not all worker aids are, however, as helpful as expected. In order to find the right tools, a better understanding of the work and physical load is needed. This thesis aims to identify which parts of the catenary construction workarethemostdemandingbycombininganergonomicriskassessment(ERA)withthedevelopment of an in situ biomechanical analysis tool. The research consists of two major components. First, a qualitative ergonomic risk assessment was conducted to identify the most physically demanding tasks in rail catenary construction. Methods in cluded site visits to Strukton’s rail construction projects, unstructured and semi-structured interviews with workers and experts, and a questionnaire administered to 12 workers. These data were used to develop a Work Breakdown Structure (WBS), categorize tasks based on their physical demand, dura tion, and frequency, and identify prevalent ergonomic risks. Tasks such as wire tensioning, removing wires, and task related to the support portal were identified as high-risk activities. Back, shoulder, and ankle complaints are the most commonly reported by workers and might be related to these tasks. Fu ture research should expand the worker sample size, investigate the causes of WRMSDs further, and enhancethe reliability of task scoring. Recommendations for Strukton include adding sidewalks on site, redesigning boom lift platforms, introducing ergonomic tool bags, and using electric tools for tasks that require high force. The second component of the research involved designing and validating an in-situ biomechanical analysis tool. The tool leverages pose detection technology from widely available mobile devices and integrates it with musculoskeletal modeling software to capture and analyze joint kinematics and mo ments during construction tasks. This approach allows for task-specific quantification of physical loads experienced by workers, bridging the gap between qualitative insights and quantitative measurements. A pilot validation study demonstrated the feasibility of the tool in providing kinematic and joint moment data for biomechanical analysis, though further validation is needed to generalize its application. Poten tial uses of the tool include automating ERAs and validating conceptual worker aids. Future research should focus on deploying the tool in real-world working environments and refining the implementation of external force data in the modeling process. The findings of this thesis highlight the critical need for task-specific ergonomic interventions in the construction of rail catenaries. The combination of qualitative ERA and biomechanical analysis offers a comprehensive framework to identify risks and design targeted solutions. This work provides a foun dation for future research into ergonomic improvements in dynamic and physically intensive industries

This research investigates methodologies to reduce and implement negative inertia in robots for upper extremity diagnostics and rehabilitation. The robot’s responsiveness is enhanced by integrating accelerometers and Kalman filters into the control scheme, ensuring smoother physical human-robot interactions. A force gain that mimics negative inertia significantly improves system dynamics within the admittance control framework. Introducing dead zones for force and acceleration stabilizes responses at lower rendered inertia, crucial for handling spastic conditions. However, this research identifies a lack of standardized evaluation methods for negative inertia and highlights hardware constraints, such as bandwidth limitations, that restrict performance. Future research should focus on establishing evaluation standards and optimizing hardware to refine control precision. This work demonstrates the potential of advanced control strategies to optimize robotic rehabilitation, paving the way for more effective diagnostic and therapeutic interventions.