Towards the adoption of wearable exoskeletons in occupational workspaces: model-based assessment and control of back-support exoskeletons
Alejandro Moya Esteban is a PhD student in the department Biomechanical Engineering. Promotors are prof.dr.ir. M. Sartori and prof.dr.ir H. van der Kooij from the Faculty of Engineering Technology.
Material handling activities such as dynamic lifting of heavy objects or prolonged forward bending postures are common in occupational workspaces such as factories, warehouses or nursing homes. These movements have been identified as risk factors for the development of musculoskeletal disorders such as low back pain. Back-support exoskeletons have been demonstrated to relieve spinal loading in the lower back musculoskeletal system. However, the adoption of back-support exoskeletons in real-life scenarios is minimal due to several factors. For instance, most models are unable to adapt their assistance levels to workers’ multiple and diverse tasks. Additionally, there is a lack of benchmarking guidelines which allow the correct assessment and comparison of different models.
This dissertation aims at enhancing the adoption of back-support exoskeletons. First, we developed a set of tools to evaluate how the human musculoskeletal system reacts to the support provided by active and passive back-support exoskeletons. In a first study, we used objective and subjective measures to assess the support provided by two rigid and two soft passive back-support exoskeletons. This study proposed, therefore, a set of benchmarking guidelines which aim at standardizing the evaluation of these devices.
Additionally, in this dissertation, we proposed electromyography-driven musculoskeletal modeling techniques to assess the biomechanical impact of back-support exoskeletons in terms of lumbosacral joint moments and compression forces. This was done under a large repertoire of symmetric, asymmetric and weight conditions, both offline and in real-time. Specifically, the proposed real-time pipeline demonstrated its potential for the development of real-time biofeedback frameworks to assess injury-related risk factors and back-support exoskeletons.
Finally, we developed a novel and intuitive human-machine interface for an active back-support exoskeleton. This interface relied on our real-time electromyography-driven methodology and derived assistive forces proportional to biological lumbosacral joint moments. This resulted in adaptive assistive forces, specific to the lifting technique, external loading conditions and actual internal forces of the user’s musculoskeletal system.
Overall, the studies included in this dissertation have the potential to facilitate the translation of wearable assistive robotic exoskeletons to real-life occupational environments.
