In industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture.

During an eight-hour shift, an industrial worker will inevitably cycle through specific postures. Those postures can cause microtrauma on the musculoskeletal system that accumulates, which in turn can lead to chronic injury. To assess how problematic a posture is, the rapid upper limb assessment (RULA) scoring system is widely employed by the industry. Even though it is a very quick and efficient method of assessment, RULA is not a biomechanics-based measurement that is anchored in a physical parameter of the human body. As such RULA does not give a detailed description of the impact each posture has on the human joints but rather, an overarching, simplified assessment of a posture. To address this issue, this paper proposes the use of joint angles and torques as an alternative way of ergonomics evaluation. The cumulative motion and torque throughout a trial is compared with the average motions and torques for the same task. This allows the evaluation of each joint's kinematic and kinetic performance while still be able to assess a task"at-a-glance". To do this, an upper human body model was created and the mass of each segment were assigned. The joint torques and the RULA scores were calculated for simple range of motion (ROM) tasks, as well as actual tasks from a TV assembly line. The joint angles and torques series were integrated and then normalized to give the kinematic and kinetic contribution of each joint during a task as a percentage. This made possible to examine each joint's strain during each task as well as highlight joints that need to be more closely examined. Results show how the joint angles and torques can identify which joint is moving more and which one is under the most strain during a task. It was also possible to compare the performance of a task with the average performance and identify deviations that may imply improper execution. Even though the RULA is a very fast and concise assessment tool, it leaves little room for further analyses. However, the proposed work suggests a richer alternative without sacrificing the benefit of a quick evaluation. The biggest limitation of this work is that a pool of proper executions needs to be recorded for each task before individual comparisons can be done.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Manual laborers from the industry sector are often subject to critical physical strain that lead to work-related musculoskeletal disorders. Lifting, poor posture and repetitive movements are among the causes of these disorders. In order to prevent them, several rules and methods have been established to identify ergonomic risks that the worker might be exposed during his/her activities. However, the ergonomic assessment though these methods is not a trivial task and a relevant degree of theoretical knowledge on the part of the analyst is necessary. Therefore in this paper, a web-based automatic ergonomic assessment module is proposed. The proposed module uses segment rotations acquired from inertial measurement units for the assessment and provides as feedback RULA scores, color visualisation and limb angles in a simple, intuitive and meaningful way. RULA is one of the most used observational methods for assessment of occupational risk factors for upper-extremity musculoskeletal disorders. By automatizing RULA an interesting perspective for extracting posture analytics for ergonomic assessment is opened, as well as the inclusion of new features that may complement it. For future work, the use of other features and sensors will be investigated for its implementation on the module.

To prevent work-related musculoskeletal disorders (WMSD) the ergonomists apply manual heuristic methods to determine when the worker is exposed to risk factors. However, these methods require an observer and the results can be subjective. This paper proposes a method to automatically evaluate the ergonomic risk factors when performing a set of postures from the ergonomic assessment worksheet (EAWS). Joint angle motion data have been recorded with a full-body motion capture system. These data modeled the motion patterns of four different risk factors, with the use of hidden Markov models (HMMs). Based on the EAWS, automated scores were assigned by the HMMs and were compared to the scores calculated manually. Because the method proposed here is intrusive and requires expensive equipment, kinematic data from a reduced set of two sensors was also evaluated.

No abstract